Gut microbiome, big data and machine learning to promote precision medicine for cancer
Abstract
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The microbiome plays a pivotal role in cancer initiation, progression, and treatment response.
“Recognition of the pivotal role of the microbiome in cancer initiation, progression, and treatment response has opened new avenues for therapeutic intervention [7–12]”
From Phage therapy in oncology: opportunities for cancer prevention and treatment (2026) · cites this paper as [9]
Cites
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,617 · see citing works
Metagenomic biomarker discovery and explanation
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192 · see citing works
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R., & Gordon, J. I. (2007) · Nature · doi:10.1038/nature06244 · Cited by 6,091 · see citing works
Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors
Routy, B., Le Chatelier, E., Derosa, L., Duong, C. P. M., Alou, M. T., Daillère, R., Fluckiger, A., Messaoudene, M., Rauber, C., Roberti, M. P., et al. (2018) · Science · doi:10.1126/science.aan3706 · Cited by 5,671 · see citing works
Diversity, stability and resilience of the human gut microbiota
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K., & Knight, R. (2012) · Nature · doi:10.1038/nature11550 · Cited by 5,533 · see citing works
Role of the microbiota in immunity and inflammation.
Belkaid, Y., & Hand, T. W. (2014) · Cell · doi:10.1016/j.cell.2014.03.011 · Cited by 5,385 · see citing works
Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients
Gopalakrishnan, V., Spencer, C. N., Nezi, L., Reuben, A., Andrews, M. C., Karpinets, T. V., Prieto, P. A., Vicente, D., Hoffman, K., Wei, S. C., et al. (2018) · Science · doi:10.1126/science.aan4236 · Cited by 4,812 · see citing works
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Helicobacter pylori Infection and the Risk of Gastric Carcinoma
Parsonnet, J., FRIEDMAN, G. D., Vandersteen, D. P., Chang, Y., Vogelman∥, J. H., Orentreich∥, N., & Sibley, R. K. (1991) · New England Journal of Medicine · doi:10.1056/nejm199110173251603 · Cited by 3,893 · see citing works
Commensal <i>Bifidobacterium</i> promotes antitumor immunity and facilitates anti–PD-L1 efficacy
Sivan, A., Corrales, L., Hubert, N., Williams, J. B., Aquino-Michaels, K., Earley, Z. M., Benyamin, F. W., Man Lei, Y., Jabri, B., Alegre, M.-L., et al. (2015) · Science · doi:10.1126/science.aac4255 · Cited by 3,888 · see citing works
Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota
Vétizou, M., Pitt, J. M., Daillère, R., Lepage, P., Waldschmitt, N., Flament, C., Rusakiewicz, S., Routy, B., Roberti, M. P., Duong, C. P. M., et al. (2015) · Science · doi:10.1126/science.aad1329 · Cited by 3,488 · see citing works
Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk
Tang, W. H. W., Wang, Z., Levison, B. S., Koeth, R. A., Britt, E. B., Fu, X., Wu, Y., & Hazen, S. L. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1109400 · Cited by 3,402 · see citing works
The Treatment-Naive Microbiome in New-Onset Crohn’s Disease
Gevers, D., Kugathasan, S., Denson, L. A., Vázquez-Baeza, Y., Van Treuren, W., Ren, B., Schwager, E., Knights, D., Song, S. J., Yassour, M., et al. (2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.02.005 · Cited by 3,220 · see citing works
Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
Lloyd-Price, J., Arze, C., Ananthakrishnan, A. N., Schirmer, M., Avila-Pacheco, J., Poon, T. W., Andrews, E., Ajami, N. J., Bonham, K. S., Brislawn, C. J., et al. (2019) · Nature · doi:10.1038/s41586-019-1237-9 · Cited by 3,143 · see citing works
The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients
Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M.-L., Luke, J. J., & Gajewski, T. F. (2018) · Science · doi:10.1126/science.aao3290 · Cited by 3,107 · see citing works
Personalized nutrition by prediction of glycemic responses
Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.001 · Cited by 2,929 · see citing works
Kostic, A. D., Chun, E., Robertson, L., Glickman, J. N., Gallini, C. A., Michaud, M., Clancy, T. E., Chung, D. C., Lochhead, P., Hold, G. L., et al. (2013) · Cell Host & Microbe · doi:10.1016/j.chom.2013.07.007 · Cited by 2,718 · see citing works
Global burden of cancers attributable to infections in 2008: a review and synthetic analysis
de Martel, C., Ferlay, J., Franceschi, S., Vignat, J., Bray, F., Forman, D., & Plummer, M. (2012) · The Lancet Oncology · doi:10.1016/s1470-2045(12)70137-7 · Cited by 2,540 · see citing works
Rubinstein, M. R., Wang, X., Liu, W., Hao, Y., Cai, G., & Han, Y. W. (2013) · Cell Host & Microbe · doi:10.1016/j.chom.2013.07.012 · Cited by 2,476 · see citing works
The gut microbiota and host health: a new clinical frontier
Marchesi, J. R., Adams, D. H., Fava, F., Hermes, G. D. A., Hirschfield, G. M., Hold, G., Quraishi, M. N., Kinross, J., Smidt, H., Tuohy, K. M., et al. (2016) · Gut · doi:10.1136/gutjnl-2015-309990 · Cited by 2,302 · see citing works
Commensal Bacteria Control Cancer Response to Therapy by Modulating the Tumor Microenvironment
Iida, N., Dzutsev, A., Stewart, C. A., Smith, L., Bouladoux, N., Weingarten, R. A., Molina, D. A., Salcedo, R., Back, T., Cramer, S., et al. (2013) · Science · doi:10.1126/science.1240527 · Cited by 2,251 · see citing works
Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome
Yoshimoto, S., Loo, T. M., Atarashi, K., Kanda, H., Sato, S., Oyadomari, S., Iwakura, Y., Oshima, K., Morita, H., Hattori, M., et al. (2013) · Nature · doi:10.1038/nature12347 · Cited by 2,219 · see citing works
Shotgun metagenomics, from sampling to analysis
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017) · Nature Biotechnology · doi:10.1038/nbt.3935 · Cited by 2,202 · see citing works
Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota
MetaHIT consortium, Forslund, K., Hildebrand, F., Nielsen, T., Falony, G., Le Chatelier, E., Sunagawa, S., Prifti, E., Vieira-Silva, S., Gudmundsdottir, V., et al. (2015) · Nature · doi:10.1038/nature15766 · Cited by 2,116 · see citing works
The Intestinal Microbiota Modulates the Anticancer Immune Effects of Cyclophosphamide
Viaud, S., Saccheri, F., Mignot, G., Yamazaki, T., Daillère, R., Hannani, D., Enot, D. P., Pfirschke, C., Engblom, C., Pittet, M. J., et al. (2013) · Science · doi:10.1126/science.1240537 · Cited by 2,050 · see citing works
Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans
Sender, R., Fuchs, S., & Milo, R. (2016) · Cell · doi:10.1016/j.cell.2016.01.013 · Cited by 2,050 · see citing works
Immune checkpoint inhibitors: recent progress and potential biomarkers
Darvin, P., Toor, S. M., Sasidharan Nair, V., & Elkord, E. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0191-1 · Cited by 2,035 · see citing works
Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030 · see citing works
Gut microbiome structure and metabolic activity in inflammatory bowel disease
Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall, A. B., Mallick, H., McIver, L. J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0306-4 · Cited by 1,995 · see citing works
Genomic analysis identifies association of <i>Fusobacterium</i> with colorectal carcinoma
Kostic, A. D., Gevers, D., Pedamallu, C. S., Michaud, M., Duke, F., Earl, A. M., Ojesina, A. I., Jung, J., Bass, A. J., Tabernero, J., et al. (2012) · Genome Research · doi:10.1101/gr.126573.111 · Cited by 1,960 · see citing works
Ghiringhelli, F., Apetoh, L., Tesniere, A., Aymeric, L., Ma, Y., Ortiz, C., Vermaelen, K., Panaretakis, T., Mignot, G., Ullrich, E., et al. (2009) · Nature Medicine · doi:10.1038/nm.2028 · Cited by 1,898 · see citing works
Dhariwal, A., Chong, J., Habib, S. M., King, I. L., Agellon, L. B., & Xia, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx295 · Cited by 1,884 · see citing works
Wu, S., Rhee, K.-J., Albesiano, E., Rabizadeh, S., Wu, X., Yen, H.-R., Huso, D. L., Brancati, F. L., Wick, E., McAllister, F., et al. (2009) · Nature Medicine · doi:10.1038/nm.2015 · Cited by 1,812 · see citing works
Composition, variability, and temporal stability of the intestinal microbiota of the elderly
Claesson, M. J., Cusack, S., O'Sullivan, O., Greene-Diniz, R., de Weerd, H., Flannery, E., Marchesi, J. R., Falush, D., Dinan, T., Fitzgerald, G., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000097107 · Cited by 1,754 · see citing works
Self-citationArtificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698 · see citing works
Schwabe, R. F., & Jobin, C. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3610 · Cited by 1,689 · see citing works
Wu, H., Esteve, E., Tremaroli, V., Khan, M. T., Caesar, R., Mannerås-Holm, L., Ståhlman, M., Olsson, L. M., Serino, M., Planas-Fèlix, M., et al. (2017) · Nature Medicine · doi:10.1038/nm.4345 · Cited by 1,632 · see citing works
Data science and its relationship to big data and data-driven decision making
Provost, F., & Fawcett, T. (2013) · Big Data · doi:10.1089/big.2013.1508 · Cited by 1,568 · see citing works
The impact of diet and lifestyle on gut microbiota and human health
Conlon, M., & Bird, A. (2014) · Nutrients · doi:10.3390/nu7010017 · Cited by 1,567 · see citing works
Privacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522 · see citing works
Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
Antibiotics, birth mode, and diet shape microbiome maturation during early life
Bokulich, N. A., Chung, J., Battaglia, T., Henderson, N., Jay, M., Li, H., D. Lieber, A., Wu, F., Perez-Perez, G. I., Chen, Y., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad7121 · Cited by 1,469 · see citing works
Immune adaptations that maintain homeostasis with the intestinal microbiota
Hooper, L. V., & Macpherson, A. J. (2010) · Nature reviews. Immunology · doi:10.1038/nri2710 · Cited by 1,455 · see citing works
Big data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works
Garrett, W. S. (2015) · Science · doi:10.1126/science.aaa4972 · Cited by 1,404 · see citing works
Pushalkar, S., Hundeyin, M., Daley, D., Zambirinis, C. P., Kurz, E., Mishra, A., Mohan, N., Aykut, B., Usyk, M., Torres, L. E., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-1134 · Cited by 1,400 · see citing works
The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy
Gopalakrishnan, V., Helmink, B. A., Spencer, C. N., Reuben, A., & Wargo, J. A. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.015 · Cited by 1,392 · see citing works
Human gut microbiome: Hopes, threats and promises
Cani, P. D. (2018) · Gut · doi:10.1136/gutjnl-2018-316723 · Cited by 1,386 · see citing works
The Integrative Human Microbiome Project
The Integrative HMP (iHMP) Research Network Consortium, Proctor, L. M., Creasy, H. H., Fettweis, J. M., Lloyd-Price, J., Mahurkar, A., Zhou, W., Buck, G. A., Snyder, M. P., Strauss, J. F., et al. (2019) · Nature · doi:10.1038/s41586-019-1238-8 · Cited by 1,381 · see citing works
Wirbel, J., Pyl, P. T., Kartal, E., Zych, K., Kashani, A., Milanese, A., Fleck, J. S., Voigt, A. Y., Palleja, A., Ponnudurai, R., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0406-6 · Cited by 1,373 · see citing works
Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations
Seidel, J. A., Otsuka, A., & Kabashima, K. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00086 · Cited by 1,360 · see citing works
Persistent gut microbiota immaturity in malnourished Bangladeshi children
Subramanian, S., Huq, S., Yatsunenko, T., Haque, R., Mahfuz, M., Alam, M. A., Benezra, A., DeStefano, J., Meier, M. F., Muegge, B. D., et al. (2014) · Nature · doi:10.1038/nature13421 · Cited by 1,270 · see citing works
Wang, Y., & Qian, P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007401 · Cited by 1,270 · see citing works
Yu, J. Q., Feng, Q., Wong, S. H., Zhang, D., Liang, Q. Y., Qin, Y., Tang, L., Zhao, H., Stenvang, J., Li, Y., et al. (2015) · Gut · doi:10.1136/gutjnl-2015-309800 · Cited by 1,259 · see citing works
Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,229 · see citing works
Alleviating Cancer Drug Toxicity by Inhibiting a Bacterial Enzyme
Wallace, B. D., Wang, H., Lane, K. T., Scott, J. E., Orans, J., Koo, J. S., Venkatesh, M., Jobin, C., Yeh, L.-A., Mani, S., et al. (2010) · Science · doi:10.1126/science.1191175 · Cited by 1,080 · see citing works
Thomas, A. M., Manghi, P., Asnicar, F., Pasolli, E., Armanini, F., Zolfo, M., Beghini, F., Manara, S., Karcher, N., Pozzi, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0405-7 · Cited by 1,030 · see citing works
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988 · see citing works
Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing
Ranjan, R., Rani, A., Metwally, A., McGee, H. S., & Perkins, D. L. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2015.12.083 · Cited by 981 · see citing works
Next-Generation Machine Learning for Biological Networks
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works
Next-generation probiotics: the spectrum from probiotics to live biotherapeutics
O’Toole, P. W., Marchesi, J. R., & Hill, C. (2017) · Nature Microbiology · doi:10.1038/nmicrobiol.2017.57 · Cited by 971 · see citing works
Gut microbiota modulation of chemotherapy efficacy and toxicity
Alexander, J. L., Wilson, I. D., Teare, J., Marchesi, J. R., Nicholson, J. K., & Kinross, J. M. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.20 · Cited by 970 · see citing works
Derosa, L., Hellmann, M. D., Spaziano, M., Halpenny, D., Fidelle, M., Rizvi, H., Long, N., Plodkowski, A. J., Arbour, K. C., Chaft, J. E., et al. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy103 · Cited by 908 · see citing works
Daillère, R., Vétizou, M., Waldschmitt, N., Yamazaki, T., Isnard, C., Poirier-Colame, V., Duong, C. P. M., Flament, C., Lepage, P., Roberti, M. P., et al. (2016) · Immunity · doi:10.1016/j.immuni.2016.09.009 · Cited by 897 · see citing works
Microbiota: a key orchestrator of cancer therapy
Roy, S., & Trinchieri, G. (2017) · Nature Reviews Cancer · doi:10.1038/nrc.2017.13 · Cited by 883 · see citing works
Microbial Dysbiosis in Colorectal Cancer (CRC) Patients
Sobhani, I., Tap, J., Roudot‐Thoraval, F., Roperch, J. P., Letulle, S., Langella, P., Corthier, G., Nhieu, J. T. V., & Furet, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016393 · Cited by 842 · see citing works
Ren, Z., Li, A., Jiang, J., Zhou, L., Yu, Z., Lu, H., Xie, H., Chen, X., Shao, L., Zhang, R., et al. (2018) · Gut · doi:10.1136/gutjnl-2017-315084 · Cited by 772 · see citing works
Microbiota as Predictor of Mortality in Allogeneic Hematopoietic-Cell Transplantation
Peled, J. U., Gomes, A. L. C., Devlin, S. M., Littmann, E. R., Taur, Y., Sung, A. D., Weber, D., Hashimoto, D., Slingerland, A. E., Slingerland, J. B., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa1900623 · Cited by 765 · see citing works
A community effort to assess and improve drug sensitivity prediction algorithms
Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works
Oxidative stress and apoptosis: Impact on cancer therapy
Ozben, T. (2007) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20874 · Cited by 751 · see citing works
Clinical pharmacokinetics and metabolism of irinotecan (CPT-11).
Mathijssen, R. H. J., van Alphen, R. J., Verweij, J., Loos, W. J., Nooter, K., Stoter, G., & Sparreboom, A. (2001) · PubMed · Cited by 744 · see citing works
Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis
Wang, Y., Wiesnoski, D. H., Helmink, B. A., Gopalakrishnan, V., Choi, K., DuPont, H. L., Jiang, Z.-D., Abu-Sbeih, H., Sanchez, C. A., Chang, C.-C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0238-9 · Cited by 740 · see citing works
The Gut Microbiome Modulates Colon Tumorigenesis
Zackular, J. P., Baxter, N. T., Iverson, K. D., Sadler, W. D., Petrosino, J. F., Chen, G., & Schloss, P. D. (2013) · mBio · doi:10.1128/mbio.00692-13 · Cited by 717 · see citing works
Relating the metatranscriptome and metagenome of the human gut
Franzosa, E. A., Morgan, X. C., Segata, N., Waldron, L., Reyes, J. A., Earl, A. M., Giannoukos, G., Boylan, M. R., Ciulla, D., Gevers, D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319284111 · Cited by 713 · see citing works
Frankel, A. E., Coughlin, L. A., Kim, J., Froehlich, T. W., Xie, Y., Frenkel, E. P., & Koh, A. Y. (2017) · Neoplasia · doi:10.1016/j.neo.2017.08.004 · Cited by 709 · see citing works
Haiser, H. J., Gootenberg, D. B., Chatman, K., Sirasani, G., Balskus, E. P., & Turnbaugh, P. J. (2013) · Science · doi:10.1126/science.1235872 · Cited by 704 · see citing works
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659 · see citing works
The oral microbiota in colorectal cancer is distinctive and predictive
Flemer, B., Warren, R. D., Barrett, M. P., Cisek, K., Das, A., Jeffery, I. B., Hurley, E., O‘Riordain, M., Shanahan, F., & O‘Toole, P. W. (2018) · Gut · doi:10.1136/gutjnl-2017-314814 · Cited by 646 · see citing works
The International Cancer Genome Consortium Data Portal
Zhang, J., Bajari, R., Andric, D., Gerthoffert, F., Lepsa, A., Nahal-Bose, H. K., Stein, L. D., & Ferretti, V. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0055-9 · Cited by 597 · see citing works
How Machine Learning Will Transform Biomedicine
Goecks, J., Jalili, V., Heiser, L. M., & Gray, J. W. (2020) · Cell · doi:10.1016/j.cell.2020.03.022 · Cited by 593 · see citing works
Gut Microbiota and Cancer: From Pathogenesis to Therapy
Vivarelli, S., Salemi, R., Candido, S., Falzone, L., Santagati, M., Stefani, S., Torino, F., Banna, G. L., Tonini, G., & Libra, M. (2019) · Cancers · doi:10.3390/cancers11010038 · Cited by 586 · see citing works
Jin, Y., Dong, H., Xia, L., Yang, Y., Zhu, Y., Shen, Y., Zheng, H., Yao, C., Wang, Y., & Lu, S. (2019) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2019.04.007 · Cited by 573 · see citing works
The human gut microbiome as a screening tool for colorectal cancer
Zackular, J. P., Rogers, M. A. M., Ruffin, M. T., & Schloss, P. D. (2014) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-14-0129 · Cited by 551 · see citing works
Growth and Development of an Infant Receiving All Nutrients Exclusively by Vein
Wilmore, D. W. (1968) · JAMA · doi:10.1001/jama.1968.03140100042009 · Cited by 543 · see citing works
Lazăr, V., Diţu, L.-M., Pîrcălăbioru, G. G., Gheorghe, I., Curuțiu, C., Holban, A. M., Picu, A. A., Petcu, L. M., & Chifiriuc, M. C. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01830 · Cited by 536 · see citing works
A new era: artificial intelligence and machine learning in prostate cancer
Goldenberg, S. L., Nir, G., & Salcudean, S. E. (2019) · Nature Reviews Urology · doi:10.1038/s41585-019-0193-3 · Cited by 523 · see citing works
A Longitudinal Big Data Approach for Precision Health
Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0414-6 · Cited by 494 · see citing works
Supervised classification of human microbiota
Knights, D., Costello, E. K., & Knight, R. D. (2010) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2010.00251.x · Cited by 482 · see citing works
Persistent microbiome alterations modulate the rate of post-dieting weight regain
Thaiss, C. A., Itav, S., Rothschild, D., Meijer, M. T., Levy, M., Moresi, C., Dohnalová, L., Braverman, S., Rozin, S., Malitsky, S., et al. (2016) · Nature · doi:10.1038/nature20796 · Cited by 471 · see citing works
Ananthakrishnan, A. N., Luo, C., Yajnik, V., Khalili, H., Garber, J. J., Stevens, B. W., Cleland, T., & Xavier, R. J. (2017) · Cell Host & Microbe · doi:10.1016/j.chom.2017.04.010 · Cited by 451 · see citing works
Rodriguez-Martin, B., Alvarez, E. G., Baez-Ortega, A., Zamora, J., Supek, F., Demeulemeester, J., Santamarina, M., Ju, Y. S., Temes, J., Garcia-Souto, D., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0562-0 · Cited by 451 · see citing works
Community differentiation of the cutaneous microbiota in psoriasis
Alekseyenko, A. V., Perez-Perez, G. I., Souza, A. D., Strober, B. E., Gao, Z., Bihan, M., Li, K., Methé, B. A., & Blaser, M. J. (2013) · Microbiome · doi:10.1186/2049-2618-1-31 · Cited by 442 · see citing works
Touchefeu, Y., Montassier, E., Nieman, K. M., Gastinne, T., Potel, G., Varannes, S. B. D., Vacon, F. L., & Cochetiere, M. F. D. L. (2014) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.12878 · Cited by 427 · see citing works
An improved pig reference genome sequence to enable pig genetics and genomics research
Warr, A., Affara, N. A., Aken, B. L., Beiki, H., Bickhart, D. M., Billis, K., Chow, W., Eöry, L., Finlayson, H. A., Flicek, P., et al. (2020) · GigaScience · doi:10.1093/gigascience/giaa051 · Cited by 427 · see citing works
A wellness study of 108 individuals using personal, dense, dynamic data clouds
Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3870 · Cited by 427 · see citing works
Learning and Retrieval Rate of Words Presented Auditorily and Visually
Brand, N., & JOLLES, J. (1985) · The Journal of General Psychology · doi:10.1080/00221309.1985.9711004 · Cited by 426 · see citing works
A review on machine learning principles for multi-view biological data integration
Li, Y., Wu, F., & Ngom, A. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw113 · Cited by 425 · see citing works
Gut mucosal virome alterations in ulcerative colitis
Zuo, T., Lu, X., Zhang, Y., Cheung, C. P., Lam, S., Zhang, F., Tang, W., Ching, J. Y. L., Zhao, R., Chan, P. K. S., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-318131 · Cited by 417 · see citing works
Osterlund, P. J., Ruotsalainen, T. M., Korpela, R., Saxelin, M., Ollus, A., Valta, P., Kouri, M., Elomaa, I., & Joensuu, H. (2007) · British Journal of Cancer · doi:10.1038/sj.bjc.6603990 · Cited by 408 · see citing works
Lung microbiota across age and disease stage in cystic fibrosis
Coburn, B. A., Wang, P. W., Caballero, J. D., Clark, S. T., Brahma, V., Donaldson, S. L., Zhang, Y., Surendra, A., Gong, Y., Tullis, D. E., et al. (2015) · Scientific Reports · doi:10.1038/srep10241 · Cited by 388 · see citing works
Thomas, S., Izard, J., Walsh, E. K., Batich, K. A., Chongsathidkiet, P., Clarke, G. A., Sela, D. A., Muller, A. J., Mullin, J. M., Albert, K., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-2929 · Cited by 369 · see citing works
The role of diet on gut microbiota composition.
Bibbò, S., Ianiro, G., Giorgio, V., Scaldaferri, F., Masucci, L., Gasbarrini, A., & Cammarota, G. (2016) · PubMed · Cited by 358 · see citing works
Self-citationDrewes, J. L., White, J. R., Dejea, C. M., Fathi, P., Iyadorai, T., Vadivelu, J., Roslani, A. C., Wick, E. C., Mongodin, E. F., Loke, M. F., et al. (2017) · npj Biofilms and Microbiomes · doi:10.1038/s41522-017-0040-3 · Cited by 335 · see citing works
Faecal microbiota transplantation protects against radiation‐induced toxicity
Cui, M., Xiao, H., Li, Y., Zhou, L., Zhao, S., Luo, D., Zheng, Q., Dong, J., Zhao, Y., Zhang, X., et al. (2017) · EMBO Molecular Medicine · doi:10.15252/emmm.201606932 · Cited by 327 · see citing works
Gerassy-Vainberg, S., Blatt, A., Danin‐Poleg, Y., Gershovich, K., Sabo, E., Nevelsky, A., Daniel, S., Dahan, A., Ziv, O., Dheer, R., et al. (2017) · Gut · doi:10.1136/gutjnl-2017-313789 · Cited by 321 · see citing works
Exploring the emerging role of the microbiome in cancer immunotherapy
Fessler, J. L., Matson, V. Z., & Gajewski, T. F. (2019) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-019-0574-4 · Cited by 318 · see citing works
Local bacteria affect the efficacy of chemotherapeutic drugs
Lehouritis, P., Cummins, J., Stanton, M., Murphy, C. T., McCarthy, F. O., Reid, G., Urbaniak, C., Byrne, W. L., & Tangney, M. (2015) · Scientific Reports · doi:10.1038/srep14554 · Cited by 313 · see citing works
Use of probiotics for prevention of radiation-induced diarrhea
Delia, P. (2007) · World Journal of Gastroenterology · doi:10.3748/wjg.v13.i6.912 · Cited by 312 · see citing works
Specific Gut Microbiome Signature Predicts the Early-Stage Lung Cancer
Zheng, Y., Fang, Z., Xue, Y., Zhang, J., Zhu, J., Gao, R., Yao, S., Ye, Y., Wang, S., Lin, C., et al. (2020) · Gut Microbes · doi:10.1080/19490976.2020.1737487 · Cited by 301 · see citing works
Lordan, C., Thapa, D., Ross, R. P., & Cotter, P. D. (2020) · Gut Microbes · doi:10.1080/19490976.2019.1613124 · Cited by 289 · see citing works
Scott, A. J., Alexander, J. L., Merrifield, C. A., Cunningham, D., Jobin, C., Brown, R., Alverdy, J., O’Keefe, S. J., Gaskins, H. R., Teare, J., et al. (2019) · Gut · doi:10.1136/gutjnl-2019-318556 · Cited by 288 · see citing works
Big Data and Data Science in Critical Care
Sanchez-Pinto, L. N., Luo, Y., & Churpek, M. M. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.04.037 · Cited by 284 · see citing works
Desired Turbulence? Gut-Lung Axis, Immunity, and Lung Cancer
Bingula, R., Filaire, M., Radosevic‐Robin, N., Bey, M., Berthon, J., Bernalier‐Donadille, A., Vasson, M., & Filaire, E. (2017) · Journal of Oncology · doi:10.1155/2017/5035371 · Cited by 272 · see citing works
Chitapanarux, I., Chitapanarux, T., Traisathit, P., Kudumpee, S., Tharavichitkul, E., & Lorvidhaya, V. (2010) · Radiation Oncology · doi:10.1186/1748-717x-5-31 · Cited by 257 · see citing works
Guidance on the management of diarrhoea during cancer chemotherapy
Andreyev, J. N., Ross, P. J., Donnellan, C. F., Lennan, E., Leonard, P., Waters, C., Wedlake, L. J., Bridgewater, J. A., Glynne‐Jones, R., Allum, W., et al. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(14)70006-3 · Cited by 257 · see citing works
Mego, M., Chovanec, J., Vochyanova, I. A., Konkolovsky, P., Mikulova, M., Rečková, M., Miškovská, V., Bystrický, B., Beniak, J., Medvecová, L., et al. (2015) · Complementary Therapies in Medicine · doi:10.1016/j.ctim.2015.03.008 · Cited by 239 · see citing works
Impact of Fecal Microbiota Transplantation on Obesity and Metabolic Syndrome—A Systematic Review
Zhang, Z., Mocanu, V., Cai, C., Dang, J. T., Slater, L., Deehan, E. C., Walter, J., & Madsen, K. L. (2019) · Nutrients · doi:10.3390/nu11102291 · Cited by 235 · see citing works
Recent advances of deep learning in bioinformatics and computational biology
Tang, B., Pan, Z., Yin, K., & Khateeb, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00214 · Cited by 233 · see citing works
Synthetic microbial consortia: from systematic analysis to construction and applications
Song, H., Ding, M., Jia, X., Ma, Q., & Yuan, Y. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00114a · Cited by 232 · see citing works
Nam, Y.-D., Kim, H. J., Seo, J.-G., Kang, S. W., & Bae, J.-W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082659 · Cited by 230 · see citing works
Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China
Ren, Z., Jiang, J., Xie, H., Li, A., Lu, H., Xu, S., Zhou, L., Zhang, H., Cui, G., Chen, X., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.18820 · Cited by 229 · see citing works
Wang, A., Ling, Z., Yang, Z., Kiela, P. R., Wang, T., Wang, C., Cao, L., Geng, F., Shen, M., Ran, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126312 · Cited by 224 · see citing works
Big data in IBD: big progress for clinical practice
Seyed Tabib, N. S., Madgwick, M., Sudhakar, P., Verstockt, B., Korcsmaros, T., & Vermeire, S. (2020) · Gut · doi:10.1136/gutjnl-2019-320065 · Cited by 223 · see citing works
Alanazi, H. O., Abdullah, A. H., & Qureshi, K. N. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0715-6 · Cited by 219 · see citing works
Elinav, E., Garrett, W. S., Trinchieri, G., & Wargo, J. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0155-3 · Cited by 215 · see citing works
Gastric Cancer as Preventable Disease
Rugge, M., Genta, R. M., Mario, F. D., El-Omar, E. M., El‐Serag, H. B., Fassan, M., Hunt, R. H., Kuipers, E. J., Malfertheiner, P., Sugano, K., et al. (2017) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2017.05.023 · Cited by 210 · see citing works
Predicting the HMA-LMA Status in Marine Sponges by Machine Learning
Moitinho‐Silva, L., Steinert, G., Nielsen, S. J., Hardoim, C. C. P., Wu, Y.-C., McCormack, G. P., López‐Legentil, S., Marchant, R., Webster, N. S., Thomas, T., et al. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00752 · Cited by 207 · see citing works
q2-sample-classifier: machine-learning tools for microbiome classification and regression
Bokulich, N. A., Dillon, M. R., Bolyen, E. K., Kaehler, B. D., Huttley, G. A., & Caporaso, J. G. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00934 · Cited by 206 · see citing works
The Gut Microbiota Predispose to the Pathophysiology of Acute Postradiotherapy Diarrhea
Manichanh, C., Varela, E., Martinez, C., Antolin, M., Llopis, M., Dor, J., Giralt, J., Guarner, F., & Malagelada, J.-R. (2008) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2008.01868.x · Cited by 201 · see citing works
Ferreira, M. R., Andreyev, J. N., Mohammed, K., Truelove, L., Gowan, S. M., Li, J., Gulliford, S., Marchesi, J. R., & Dearnaley, D. P. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0960 · Cited by 199 · see citing works
The influence of proton pump inhibitors and other commonly used medication on the gut microbiota
Imhann, F., Vila, A. V., Bonder, M. J., Manosalva, A. G. L., Koonen, D. P. Y., Fu, J., Wijmenga, C., Zhernakova, A., & Weersma, R. K. (2017) · Gut Microbes · doi:10.1080/19490976.2017.1284732 · Cited by 195 · see citing works
A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction
Zhou, Y., & Gallins, P. J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00579 · Cited by 192 · see citing works
Experimental design and quantitative analysis of microbial community multiomics
Mallick, H., Ma, S., Franzosa, E. A., Vatanen, T., Morgan, X. C., & Huttenhower, C. (2017) · Genome Biology · doi:10.1186/s13059-017-1359-z · Cited by 192 · see citing works
Qian, Z., Li, Y., Wang, Y., Li, L., Li, R., Wang, K., Li, S., Tang, K., Zhang, C., Fan, X., et al. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.02.054 · Cited by 189 · see citing works
Clostridium difficile infection and antibiotic-associated diarrhoea
Mullish, B. H., & Williams, H. R. T. (2018) · Clinical Medicine · doi:10.7861/clinmedicine.18-3-237 · Cited by 181 · see citing works
Shah, M. S., DeSantis, T. Z., Weinmaier, T., McMurdie, P. J., Cope, J. L., Altrichter, A., Yamal, J.-M., & Hollister, E. B. (2018) · Gut · doi:10.1136/gutjnl-2016-313189 · Cited by 180 · see citing works
The challenges of integrating multi-omic data sets
Palsson, B., & Zengler, K. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.462 · Cited by 178 · see citing works
Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes
Kreznar, J. H., Keller, M. P., Traeger, L. L., Rabaglia, M. E., Schueler, K. L., Stapleton, D. S., Zhao, W., Vivas, E. I., Yandell, B. S., Broman, A. T., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.01.062 · Cited by 177 · see citing works
Saito, H., Aoki, T., Aoyama, K., Kato, Y., Tsuboi, A., Yamada, A., Fujishiro, M., Oka, S., Ishihara, S., Matsuda, T., et al. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.01.054 · Cited by 171 · see citing works
Psychotropics and the Microbiome: a Chamber of Secrets…
Cussotto, S., Clarke, G. A., Dinan, T. G., & Cryan, J. F. (2019) · Psychopharmacology · doi:10.1007/s00213-019-5185-8 · Cited by 169 · see citing works
Microbial community function and biomarker discovery in the human microbiome
Segata, N., Abubucker, S., Goll, J. B., Schubert, A. M., Izard, J., Cantarel, B. L., Rodriguez-Mueller, B., Waldron, L., Zucker, J., Thiagarajan, M., et al. (2011) · Genome biology · doi:10.1186/1465-6906-12-s1-p47 · Cited by 163 · see citing works
Artificial Intelligence-Assisted Gastroenterology— Promises and Pitfalls
Ruffle, J. K., Farmer, A. D., & Aziz, Q. (2018) · The American Journal of Gastroenterology · doi:10.1038/s41395-018-0268-4 · Cited by 161 · see citing works
Kumagai, T., Rahman, F. Z., & Smith, A. M. (2018) · Nutrients · doi:10.3390/nu10101405 · Cited by 160 · see citing works
Armour, C. R., Nayfach, S., Pollard, K. S., & Sharpton, T. J. (2019) · mSystems · doi:10.1128/msystems.00332-18 · Cited by 158 · see citing works
Big data in IBD: a look into the future
Olivera, P., Danese, S., Jay, N., Natoli, G., & Peyrin-Biroulet, L. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0102-5 · Cited by 151 · see citing works
Sequence-Based Discovery of Bradyrhizobium enterica in Cord Colitis Syndrome
Bhatt, A. S., Freeman, S. S., Herrera, A. F., Pedamallu, C. S., Gevers, D., Duke, F. F., Jung, J., Michaud, M., Walker, B. J., Young, S., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211115 · Cited by 151 · see citing works
Linking Gut Microbiota to Colorectal Cancer
Raskov, H., Burcharth, J., & Pommergaard, H. (2017) · Journal of Cancer · doi:10.7150/jca.20497 · Cited by 151 · see citing works
Wada, M., Nagata, S., Saito, M., Shimizu, T., Yamashiro, Y., Matsuki, T., Asahara, T., & Nomoto, K. (2009) · Supportive Care in Cancer · doi:10.1007/s00520-009-0711-6 · Cited by 150 · see citing works
Predicting drug-target interaction network using deep learning model
You, J., McLeod, R. D., & Hu, P. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.03.016 · Cited by 150 · see citing works
Thompson, J. C., Johansen, R., Dunbar, J., & Munsky, B. E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215502 · Cited by 144 · see citing works
Vuik, F. E. R., Dicksved, J., Lam, S., Fuhler, G. M., van der Laan, L. J. W., van de Winkel, A., Konstantinov, S., Spaander, M., Peppelenbosch, M. P., Engstrand, L. G., et al. (2019) · United European Gastroenterology Journal · doi:10.1177/2050640619852255 · Cited by 140 · see citing works
Sato, M., Morimoto, K., Kajihara, S., Tateishi, R., Shiina, S., Koike, K., & Yatomi, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-44022-8 · Cited by 138 · see citing works
A review of supervised machine learning applied to ageing research
Fabris, F., de Magalhães, J. P., & Freitas, A. A. (2017) · Biogerontology · doi:10.1007/s10522-017-9683-y · Cited by 136 · see citing works
An unsupervised machine learning method for discovering patient clusters based on genetic signatures
Lopez, C. E., Tucker, S. M., Salameh, T. J., & Tucker, C. S. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.004 · Cited by 134 · see citing works
Lui, T. K.-L., Guo, C., & Leung, W. K. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.02.033 · Cited by 126 · see citing works
Zhao, W., Yang, J., Ni, B., Bi, D., Sun, Y., Xu, M., Zhu, X., Li, C., Jin, L., Gao, P., et al. (2019) · Cancer Medicine · doi:10.1002/cam4.2233 · Cited by 126 · see citing works
Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125 · see citing works
Lessons Learned from the Irinotecan Metabolic Pathway
Mader, W. F., & McLeod, H. L. (2003) · Current Medicinal Chemistry · doi:10.2174/0929867033368619 · Cited by 122 · see citing works
Mottini, C., Napolitano, F., Li, Z., Gao, X., & Cardone, L. (2019) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2019.09.023 · Cited by 122 · see citing works
Microbiota in cancer development and treatment
Raza, M. H., Gul, K., Arshad, A., Riaz, N., Waheed, U., Rauf, A., Aldakheel, F. M., Alduraywish, S. A., Rehman, M. U., Abdullah, M., et al. (2018) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-018-2816-0 · Cited by 120 · see citing works
Tseng, Y., Huang, C.-E., Wen, C.-N., Lai, P.-Y., Wu, M., Sun, Y.-C., Wang, H., & Lu, J. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.05.003 · Cited by 117 · see citing works
Development of genome-derived tumor type prediction to inform clinical cancer care
Penson, A., Camacho, N., Zheng, Y., Varghese, A. M., Al-Ahmadie, H., Razavi, P., Chandarlapaty, S., Vallejo, C. E., Vakiani, E., Gilewski, T., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2019.3985 · Cited by 115 · see citing works
LaPierre, N., Ju, C. J. ‐., Zhou, G., & Wang, W. W. (2019) · Methods · doi:10.1016/j.ymeth.2019.03.003 · Cited by 114 · see citing works
Maguire, M., & Maguire, G. (2018) · Reviews in the Neurosciences · doi:10.1515/revneuro-2018-0024 · Cited by 111 · see citing works
Wei, D., Heus, P., van de Wetering, F. T., van Tienhoven, G., Verleye, L., & Scholten, R. J. P. M. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd008831.pub3 · Cited by 108 · see citing works
Liu, M.-M., Li, S., Shu, Y., & Zhan, H.-Q. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178870 · Cited by 106 · see citing works
Reyna-Figueroa, J., Barrón‐Calvillo, E. E., García-Parra, C., Galindo-Delgado, P., Contreras-Ochoa, C. O., Lagunas-Martínez, A., Romero, F. H. C., Silva-Estrada, J. A., & Limón-Rojas, A. E. (2019) · Journal of Pediatric Hematology/Oncology · doi:10.1097/mph.0000000000001497 · Cited by 106 · see citing works
Perkins, B. A., Caskey, C. T., Brar, P., Dec, E., Karow, D. S., Kahn, A. M., Hou, Y. C., Shah, N., Boeldt, D., Coughlin, E., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706096114 · Cited by 104 · see citing works
Microbial Diversity in Clinical Microbiome Studies: Sample Size and Statistical Power Considerations
Casals‐Pascual, C., González, A., Vázquez‐Baeza, Y., Song, S. J., Jiang, L., & Knight, R. D. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.11.305 · Cited by 101 · see citing works
Metabolome of human gut microbiome is predictive of host dysbiosis
Larsen, P. E., & Dai, Y. D. (2015) · GigaScience · doi:10.1186/s13742-015-0084-3 · Cited by 100 · see citing works
Modulation of cytokine patterns and microbiome during pregnancy in IBD
van der Giessen, J., Binyamin, D., Belogolovski, A., Frishman, S., Tenenbaum-Gavish, K., Hadar, E., Louzoun, Y., Peppelenbosch, M. P., van der Woude, C. J., Koren, O., et al. (2019) · Gut · doi:10.1136/gutjnl-2019-318263 · Cited by 99 · see citing works
The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1
Imhann, F., van der Velde, K. J., Barbieri, R., Alberts, R., Voskuil, M. D., Vila, A. V., Collij, V., Spekhorst, L. M., van der Sloot, K. W. J., Peters, V., et al. (2019) · BMC Gastroenterology · doi:10.1186/s12876-018-0917-5 · Cited by 98 · see citing works
TLR Signaling Modulates Side Effects of Anticancer Therapy in the Small Intestine
Frank, M., Hennenberg, E. M., Eyking, A., Rünzi, M., Gerken, G., Scott, P., Parkhill, J., Walker, A. W., & Cario, E. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1402481 · Cited by 97 · see citing works
Effects of probiotics on chemotherapy in patients with lung cancer
Tian, Y., Li, M., Song, W., Jiang, R., & Li, Y. (2019) · Oncology Letters · doi:10.3892/ol.2019.9906 · Cited by 95 · see citing works
Gut Microbiota-Immune System Crosstalk and Pancreatic Disorders
Pagliari, D., Saviano, A., Newton, E. E., Serricchio, M., Lago, A. D., Gasbarrini, A., & Cianci, R. (2018) · Mediators of Inflammation · doi:10.1155/2018/7946431 · Cited by 92 · see citing works
Self-citationComputer-aided biomarker discovery for precision medicine: data resources, models and applications
Lin, Y., Qian, F., Shen, L., Chen, F., Chen, J., & Shen, B. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx158 · Cited by 91 · see citing works
Grewal, J. K., Tessier-Cloutier, B., Jones, M., Gakkhar, S., Ma, Y., Moore, R., Mungall, A. J., Zhao, Y., Taylor, M. D., Gelmon, K., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.2597 · Cited by 90 · see citing works
Leveraging Human Microbiome Features to Diagnose and Stratify Children with Irritable Bowel Syndrome
Hollister, E. B., Oezguen, N., Chumpitazi, B. P., Luna, R. A., Weidler, E. M., Rubio-Gonzales, M., Dahdouli, M., Cope, J. L., Mistretta, T., Raza, S., et al. (2019) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2019.01.006 · Cited by 89 · see citing works
Gut microbiota, chemotherapy and the host: the influence of the gut microbiota on cancer treatment
Pouncey, A. L., Scott, A. J., Alexander, J. L., Marchesi, J. R., & Kinross, J. K. (2018) · ecancermedicalscience · doi:10.3332/ecancer.2018.868 · Cited by 88 · see citing works
Yue, B., Luo, X., Yu, Z., Mani, S., Wang, Z., & Dou, W. (2019) · Microorganisms · doi:10.3390/microorganisms7100440 · Cited by 86 · see citing works
Machine learning methods for developing precision treatment rules with observational data
Kessler, R. C., Bossarte, R. M., Luedtke, A. R., Zaslavsky, A. M., & Zubizarreta, J. R. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103412 · Cited by 84 · see citing works
Biological insights through omics data integration
Noor, E., Cherkaoui, S., & Sauer, U. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.007 · Cited by 83 · see citing works
Enzymatic Removal of Bilirubin From Blood: A Potential Treatment for Neonatal Jaundice
Lavin, A., Sung, C., Klibanov, A. M., & Langer, R. (1985) · Science · doi:10.1126/science.4048947 · Cited by 83 · see citing works
Large-scale machine learning for metagenomics sequence classification
Vervier, K., Mahé, P., Tournoud, M., Veyrieras, J., & Vert, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv683 · Cited by 81 · see citing works
Pancreatic cyst fluid harbors a unique microbiome
Li, S., Fuhler, G. M., Nahush, B. N., Jose, T., Bruno, M. J., Peppelenbosch, M. P., & Konstantinov, S. R. (2017) · Microbiome · doi:10.1186/s40168-017-0363-6 · Cited by 80 · see citing works
Wang, Y.-H., Yao, N., Wei, K.-K., Jiang, L., Hanif, S., Wang, Z.-X., & Pei, C.-X. (2016) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2016.102 · Cited by 77 · see citing works
Pellegrini, M., Ippolito, M., Monge, T., Violi, R., Cappello, P., Ferrocino, I., Cocolin, L., Francesco, A. D., Bo, S., & Finocchiaro, C. (2020) · Nutrition · doi:10.1016/j.nut.2020.110749 · Cited by 77 · see citing works
A critical perspective on guidelines for responsible and trustworthy artificial intelligence
Buruk, B., Ekmekçi, P. E., & Arda, B. (2020) · Medicine Health Care and Philosophy · doi:10.1007/s11019-020-09948-1 · Cited by 76 · see citing works
Paik, E. S., Lee, J., Park, J., Kim, J.-H., Kim, M., Kim, T., Choi, C. H., Kim, B., Bae, D., & Seo, S. W. (2019) · Journal of Gynecologic Oncology · doi:10.3802/jgo.2019.30.e65 · Cited by 75 · see citing works
Shaikh, F. Y., Gills, J. J., & Sears, C. L. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.076 · Cited by 75 · see citing works
Vangay, P., Hillmann, B., & Knights, D. (2019) · GigaScience · doi:10.1093/gigascience/giz042 · Cited by 72 · see citing works
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
Veselkov, K., Gonzalez, G., Aljifri, S., Galea, D., Mirnezami, R., Youssef, J., Bronstein, M. M., & Laponogov, I. (2019) · Scientific Reports · doi:10.1038/s41598-019-45349-y · Cited by 71 · see citing works
Zhang, Y., Pechal, J. L., Schmidt, C. J., Jordan, H. R., Wang, W., Benbow, M. E., Sze, S., & Tarone, A. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213829 · Cited by 67 · see citing works
The human microbiome and metabolomics: Current concepts and applications
Daliri, E. B., Wei, S., Oh, D., & Lee, B. H. (2016) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2016.1220913 · Cited by 63 · see citing works
Feng, Q.-X., Liu, C. X., Qi, L., Sun, S., Song, Y. S., Yang, G., Zhang, Y., & Liu, X.-S. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.12.017 · Cited by 63 · see citing works
Henson, C., Burden, S. T., Davidson, S. E., & Lal, S. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009896.pub2 · Cited by 61 · see citing works
Flagged first, then by citations
2 of the 250 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932 · see citing works
Targeting the gut and tumor microbiota in cancer
Park, E. M., Chelvanambi, M., Bhutiani, N., Kroemer, G., Zitvogel, L., & Wargo, J. A. (2022) · Nature Medicine · doi:10.1038/s41591-022-01779-2 · Cited by 496 · see citing works
Immunological mechanisms of inflammatory diseases caused by gut microbiota dysbiosis: A review
Zhao, M., Chu, J., Feng, S., Guo, C., Xue, B., He, K., & Li, L. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114985 · Cited by 466 · see citing works
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.12.009 · Cited by 474 · see citing works
Applied body-fluid analysis by wearable devices
Brasier, N., Wang, J., Gao, W. F., Sempionatto, J. R., Dincer, C., Ates, H. C., Güder, F., Olenik, S., Schauwecker, I., Schaffarczyk, D., et al. (2024) · Nature · doi:10.1038/s41586-024-08249-4 · Cited by 316 · see citing works
Drug-microbiota interactions: an emerging priority for precision medicine
Zhao, Q. C., Chen, Y., Huang, W., Zhou, H., & Zhang, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01619-w · Cited by 296 · see citing works
Chakraborty, C., Bhattacharya, M., Pal, S., & Lee, S. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100164 · Cited by 295 · see citing works
Intratumoural microbiota: a new frontier in cancer development and therapy
Cao, Y., Xia, H., Tan, X., Shi, C., Ma, Y., Meng, D., Zhou, M., Lv, Z., Wang, S., & Jin, Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01693-0 · Cited by 263 · see citing works
Stein-Thoeringer, C. K., Saini, N. Y., Zamir, E., Blumenberg, V., Schubert, M.-L., Mor, U., Fante, M. A., Schmidt, S., Hayase, E., Hayase, T., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02234-6 · Cited by 255 · see citing works
New Insights Into the Cancer–Microbiome–Immune Axis: Decrypting a Decade of Discoveries
Jain, T., Sharma, P., Are, A. C., Vickers, S. M., & Dudeja, V. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.622064 · Cited by 184 · see citing works
Machine Learning in Nutrition Research
Kirk, D., Kok, E. J., Tufano, M., Teki̇nerdoğan, B., Feskens, E. J. M., & Camps, G. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac103 · Cited by 194 · see citing works
Human Health during Space Travel: State-of-the-Art Review
Krittanawong, C., Singh, N. K., Scheuring, R. A., Urquieta, E., Bershad, E. M., Macaulay, T. R., Kaplin, S. L., Dunn, C., Kry, S. F., Russomano, T., et al. (2022) · Cells · doi:10.3390/cells12010040 · Cited by 178 · see citing works
Utilization of the microbiome in personalized medicine
Ratiner, K., Ciocan, D. M., Abdeen, S. K., & Elinav, E. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00998-9 · Cited by 178 · see citing works
Mendoza-Suárez, M. A., Andersen, S. U., Poole, P. S., & Sánchez‐Cañizares, C. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.690567 · Cited by 159 · see citing works
Machine learning approaches in microbiome research: challenges and best practices
Παπουτσόγλου, Γ., Tarazona, S., Lopes, M. B., Klammsteiner, T., Ibrahimi, E., Eckenberger, J., Novielli, P., Tonda, A. P., Mihajlović, A., Shigdel, R., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1261889 · Cited by 145 · see citing works
Self-citationApplication of Artificial Intelligence in Lung Cancer
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138 · see citing works
Beneficial microorganisms: Regulating growth and defense for plant welfare
Liu, Y., Shi, A., Chen, Y., Xu, Z., Liu, Y., Yao, Y., Wang, Y., & Jia, B. (2024) · Plant Biotechnology Journal · doi:10.1111/pbi.14554 · Cited by 143 · see citing works
Scientists’ call to action: Microbes, planetary health, and the Sustainable Development Goals
Crowther, T. W., Rappuoli, R., Corinaldesi, C., Danovaro, R., Donohue, T. J., Huisman, J., Stein, L. Y., Timmis, K. N., Timmis, K. N., Anderson, M. Z., et al. (2024) · Cell · doi:10.1016/j.cell.2024.07.051 · Cited by 129 · see citing works
How Do Machines Learn? Artificial Intelligence as a New Era in Medicine
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., & Mackiewicz, A. A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11010032 · Cited by 124 · see citing works
Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go
Maria, R. D., Sante, G. D., Piro, G., Carbone, C., Tortora, G., Boldrini, L., Pietragalla, A., Daniele, G., Tredicine, M., Cesario, A., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11030216 · Cited by 119 · see citing works
Self-citationZhang, Q., Zhang, Z., Lu, T., Yu, Y., Peñuelas, J., Zhu, Y., & Qian, H. (2021) · Microbiome · doi:10.1186/s40168-021-01150-6 · Cited by 118 · see citing works
Gibbons, S. M., Gurry, T., Lampe, J. W., Chakrabarti, A., Dam, V., Everard, A., Goas, A., Gross, G., Kleerebezem, M., Lane, J. A., et al. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac075 · Cited by 113 · see citing works
Abdoul‐Latif, F. M., Ainane, A., Aboubaker, I. H., Jalludin, M., & Ainane, T. (2023) · Pharmaceuticals · doi:10.3390/ph16081086 · Cited by 110 · see citing works
Mollaei, M., Hassan, Z. M., Khorshidi, F., & Langroudi, L. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101056 · Cited by 110 · see citing works
The state of the art for artificial intelligence in lung digital pathology
Viswanathan, V. S., Toro, P., Corredor, G., Mukhopadhyay, S., & Madabhushi, A. (2022) · The Journal of Pathology · doi:10.1002/path.5966 · Cited by 105 · see citing works
Wu, X., Zhou, Q., Li, M., & Hu, X. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.129487 · Cited by 96 · see citing works
Machine learning-enabled optimization of extrusion-based 3D printing
Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94 · see citing works
Harnessing machine learning for development of microbiome therapeutics
McCoubrey, L. E., Elbadawi, M., Orlu, M., Gaisford, S., & Basit, A. W. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1872323 · Cited by 94 · see citing works
Chen, Y., Wang, H., Lu, W., Wu, T., Yuan, W., Zhu, J., Lee, Y. K., Zhao, J., Zhang, H., & Chen, W. (2022) · Gut Microbes · doi:10.1080/19490976.2021.2025016 · Cited by 94 · see citing works
Rodríguez-Arrastia, M. J., Martinez‐Ortigosa, A., Rueda‐Ruzafa, L., Ayora, A. F., & Ropero‐Padilla, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18084265 · Cited by 92 · see citing works
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91 · see citing works
The Next Generation Fecal Microbiota Transplantation: To Transplant Bacteria or Virome
Yu, Y., Wang, W., & Zhang, F. (2023) · Advanced Science · doi:10.1002/advs.202301097 · Cited by 94 · see citing works
Li, Q., Yang, H., Wang, P., Liu, X., Lv, K., & Ye, M. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03369-9 · Cited by 90 · see citing works
Abdelhalim, H., Berber, A., Lodi, M. K., Jain, R., Nair, A. S., Pappu, A., Patel, K., Venkat, V., Venkatesan, C., Wable, R., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.929736 · Cited by 98 · see citing works
Huang, R., Liu, Z., Sun, T., & Zhu, L. (2024) · Microbiological Research · doi:10.1016/j.micres.2024.127857 · Cited by 105 · see citing works
Predicting drug-microbiome interactions with machine learning
McCoubrey, L. E., Gaisford, S., Orlu, M., & Basit, A. W. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107797 · Cited by 80 · see citing works
Gut microbiota: A magical multifunctional target regulated by medicine food homology species
Zuo, W., Pang, Q., Yao, L.-P., Zhang, Y., Peng, C. Y., Huang, W., & Han, B. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.05.011 · Cited by 81 · see citing works
Wu, J., Singleton, S. S., Bhuiyan, U., Krammer, L., & Mazumder, R. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1337373 · Cited by 83 · see citing works
Abavisani, M., Khoshrou, A., Foroushan, S. K., Ebadpour, N., & Sahebkar, A. (2024) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2024.100211 · Cited by 75 · see citing works
An overview of machine learning methods for monotherapy drug response prediction
Firoozbakht, F., Yousefi, B., & Schwikowski, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab408 · Cited by 69 · see citing works
Aziz, R. M. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02555-7 · Cited by 69 · see citing works
Omics in gut microbiome analysis
Whon, T. W., Shin, N., Kim, J. Y., & Roh, S. W. (2021) · The Journal of Microbiology · doi:10.1007/s12275-021-1004-0 · Cited by 65 · see citing works
Han, X., Liu, Q., Li, Y., Zhang, M., Liu, K., Kwok, L.-Y., Zhang, H., & Zhang, W. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104938 · Cited by 64 · see citing works
Johnson, D., Letchumanan, V., Thum, C. C., Thurairajasingam, S., & Lee, L. (2023) · Nutrients · doi:10.3390/nu15061382 · Cited by 65 · see citing works
The transition from genomics to phenomics in personalized population health
Yurkovich, J. T., Evans, S. J., Rappaport, N., Boore, J. L., Lovejoy, J. C., Price, N. D., & Hood, L. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00674-x · Cited by 61 · see citing works
Artificial Intelligence: The Milestone in Modern Biomedical Research
Athanasopoulou, K., Daneva, G. N., Adamopoulos, P. G., & Scorilas, A. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2040049 · Cited by 60 · see citing works
Microbiome as a biomarker and therapeutic target in pancreatic cancer
Pourali, G., Kazemi, D., Chadeganipour, A. S., Arastonejad, M., Kashani, S. N., Pourali, R., Maftooh, M., Akbarzade, H., Fiuji, H., Hassanian, S. M., et al. (2024) · BMC Microbiology · doi:10.1186/s12866-023-03166-4 · Cited by 60 · see citing works
Hepatocellular Carcinoma: How the Gut Microbiota Contributes to Pathogenesis, Diagnosis, and Therapy
Luo, W., Guo, S., Zhou, Y., Zhao, J., Wang, M., Sang, L.-X., Chang, B., & Wang, B. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.873160 · Cited by 59 · see citing works
Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk
Hemmati, M. A., Monemi, M., Asli, S., Mohammadi, S., Foroozanmehr, B., Haghmorad, D., Oksenych, V., & Eslami, M. (2024) · Cells · doi:10.3390/cells13231987 · Cited by 58 · see citing works
Domain adaptation in small-scale and heterogeneous biological datasets
Orouji, S., Liu, M. C., Korem, T., & Peters, M. A. K. (2024) · Science Advances · doi:10.1126/sciadv.adp6040 · Cited by 59 · see citing works
Active Machine learning for formulation of precision probiotics
McCoubrey, L. E., Seegobin, N., Elbadawi, M., Hu, Y., Orlu, M., Gaisford, S., & Basit, A. W. (2022) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2022.121568 · Cited by 56 · see citing works
Ghaffari, P., Shoaie, S., & Nielsen, L. K. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03365-z · Cited by 58 · see citing works
Jiang, Y., Wang, C., & Zhou, S. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.005 · Cited by 62 · see citing works
Cancer chemoprevention: signaling pathways and strategic approaches
Ren, J., Yan, G., Yang, L., Kong, L., Guan, Y., Sun, H., Liu, C., Liu, L., Han, Y., & Wang, X. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02167-1 · Cited by 62 · see citing works
Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment
Su, X. (2021) · mSystems · doi:10.1128/msystems.00363-21 · Cited by 56 · see citing works
How the gut parasitome affects human health
Ianiro, G., Iorio, A., Porcari, S., Masucci, L., Sanguinetti, M., Perno, C. F., Gasbarrini, A., Putignani, L., & Cammarota, G. (2022) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848221091524 · Cited by 56 · see citing works
Self-citationWang, C., Hao, T., Wang, Z., Lin, H. L., Wei, W., Hu, Y., Wang, S., Shi, X., & Guo, Z. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.133672 · Cited by 51 · see citing works
A new era in healthcare: The integration of artificial intelligence and microbial
Huo, D.-L., & Wang, X. (2024) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2024.100319 · Cited by 51 · see citing works
Filippou, C., Themistocleous, S., Marangos, G., Panayiotou, Y., Fyrilla, M., Kousparou, C. A., Pana, Z. D., Tsioutis, C., Johnson, E. O., & Yiallouris, A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25021110 · Cited by 49 · see citing works
Ma, Y., Qiao, Y., Chen, M., Rui, D., Zhang, X., Liu, W., & Ye, L. (2024) · Water Research · doi:10.1016/j.watres.2024.123041 · Cited by 49 · see citing works
Cai, H., Li, M., Deng, R., Wang, M., & Shi, Y. (2022) · Biomarker Research · doi:10.1186/s40364-022-00413-0 · Cited by 46 · see citing works
Xia, L., Zhu, X., Wang, Y., & Lü, S. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.217123 · Cited by 48 · see citing works
Integrating the human microbiome in the forensic toolkit: Current bottlenecks and future solutions
López, C. D., Vidaki, A., & Kayser, M. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102627 · Cited by 45 · see citing works
Das, A., Behera, R. N., Kapoor, A., & Ambatipudi, K. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c03834 · Cited by 44 · see citing works
Precision medicine journey through omics approach
Hasanzad, M., Sarhangi, N., Chimeh, S. E., Ayati, N., Afzali, M., Khatami, F., Nikfar, S., & Meybodi, H. R. A. (2021) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-021-00913-0 · Cited by 44 · see citing works
Zeng, F., Su, X., Liang, X., Liao, M., Zhong, H., Xu, J., Gou, W., Zhang, X., Shen, L., Zheng, J., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03317-y · Cited by 43 · see citing works
Bellando-Randone, S., Russo, E., Venerito, V., Matucci‐Cerinic, M., Iannone, F., Tangaro, S., & Amedei, A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11070625 · Cited by 43 · see citing works
Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes
Tan, H., Shi, Y., Yue, T., Zheng, D., Luo, S., Weng, J., & Zheng, X. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.11.025 · Cited by 42 · see citing works
From hype to hope: Considerations in conducting robust microbiome science
McGuinness, A. J., Stinson, L. F., Snelson, M., Loughman, A., Stringer, A. M., Hannan, A. J., Cowan, C. S. M., Jama, H. A., Caparros-Martin, J. A., West, M. L., et al. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.09.022 · Cited by 43 · see citing works
The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome
Su, Q., Lau, R. I., Liu, Q., Li, M. K. T., Mak, J. W. Y., Lu, W., Lau, I. S. F., Lau, L. H. S., Yeung, G. T. Y., Cheung, C. P., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.04.005 · Cited by 41 · see citing works
An efficient and scalable top-down method for predicting structures of microbial communities
Ansari, A. F., Reddy, Y. B. S., Raut, J. S., & Dixit, N. M. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00131-x · Cited by 41 · see citing works
Microbiome and pancreatic cancer: time to think about chemotherapy
de Castilhos, J., Tillmanns, K., Blessing, J., Larano, A., Borisov, V., & Stein‐Thoeringer, C. K. (2024) · Gut Microbes · doi:10.1080/19490976.2024.2374596 · Cited by 41 · see citing works
Modeling Microbial Communities: Perspective and Challenges
Raajaraam, L., & Raman, K. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00116 · Cited by 40 · see citing works
Computational methods and challenges in analyzing intratumoral microbiome data
Wang, Q., Liu, Z., Ma, A., Li, Z., Liu, B., & Ma, Q. (2023) · Trends in Microbiology · doi:10.1016/j.tim.2023.01.011 · Cited by 37 · see citing works
Xiao, X., Fu, L., Zhang, Z., Tu, Z., Shen, N., & Fan, S. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c08522 · Cited by 38 · see citing works
Learning sparse log-ratios for high-throughput sequencing data
Gordon-Rodriguez, E., Quinn, T. P., & Cunningham, J. P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab645 · Cited by 37 · see citing works
Wang, X., Xiao, Y., Xu, X., Guo, L., Yu, Y., Li, N., & Xu, C. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.711884 · Cited by 35 · see citing works
Cuckoo Search-Based Optimization for Cancer Classification: A New Hybrid Approach
Aziz, R. M. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0410 · Cited by 35 · see citing works
Artificial intelligence in food biotechnology: trends and perspectives
Amore, A., & Philip, S. (2023) · Frontiers in Industrial Microbiology · doi:10.3389/finmi.2023.1255505 · Cited by 34 · see citing works
Obijuru, A., Arowoogun, J. O., Onwumere, C., Odilibe, I. P., Anyanwu, E. C., & Daraojimba, A. I. (2024) · International Medical Science Research Journal · doi:10.51594/imsrj.v4i2.810 · Cited by 34 · see citing works
Biosensors for point-of-care testing and personalized monitoring of gastrointestinal microbiota
Ngashangva, L., & Chattopadhyay, S. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1114707 · Cited by 34 · see citing works
Bacillus licheniformis Reshapes the Gut Microbiota to Alleviate the Subhealth
Feng, S.-Y., Meng, C., Hao, Z.-K., & Liu, H. (2022) · Nutrients · doi:10.3390/nu14081642 · Cited by 31 · see citing works
Method development for cross-study microbiome data mining: Challenges and opportunities
Su, X., Jing, G., Zhang, Y., & Wu, S. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.07.020 · Cited by 31 · see citing works
Fecal Microbiota Transplantation in Gastrointestinal and Extraintestinal Disorders
Ianiro, G., Segal, J. P., Mullish, B. H., Quraishi, M. N., Porcari, S., Fabiani, G., Gasbarrini, A., & Cammarota, G. (2020) · Future Microbiology · doi:10.2217/fmb-2020-0061 · Cited by 31 · see citing works
Self-citationNext-generation probiotics and engineered BEVs for precision therapeutics in osteoporosis
Wei, J., Ding, W., Song, K., Zhang, Y.-K., Luo, Q., & Qi, C. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1581971 · Cited by 28 · see citing works
Microbial source tracking using metagenomics and other new technologies
Raza, S., Kim, J., Sadowsky, M. J., & Unno, T. (2021) · The Journal of Microbiology · doi:10.1007/s12275-021-0668-9 · Cited by 30 · see citing works
Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review
Zeng, T., Yu, X., & Chen, Z. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15503 · Cited by 30 · see citing works
Mathematical-based microbiome analytics for clinical translation
Narayana, J. K., Aogáin, M. M., Goh, W. W. B., Xia, K., Tsaneva-Atanasova, K. T., & Chotirmall, S. H. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.11.029 · Cited by 30 · see citing works
McDowell, A., Kang, J., Yang, J., Jung, J., Oh, Y., Kym, S.-M., Shin, T.-S., Kim, T., Jee, Y., & Kim, Y. (2022) · Experimental & Molecular Medicine · doi:10.1038/s12276-022-00846-5 · Cited by 30 · see citing works
Coşkun, A., Ertaylan, G., Pusparum, M., Hoof, R. V., Kaya, Z. Z., Khosravi, A., & Zarrabi, A. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167339 · Cited by 29 · see citing works
Towards multi-label classification: Next step of machine learning for microbiome research
Wu, S., Chen, Y., Li, Z., Li, J., Zhao, F., & Su, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.054 · Cited by 29 · see citing works
Kang, J., Zhang, Z., Chen, Y., Zhou, Z., Zhang, J., Xu, N., Zhang, Q., Lu, T., Peijnenburg, W. J. G. M., & Qian, H. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154412 · Cited by 29 · see citing works
Zang, J., Lin, T., Xu, C., Lin, Y., Ma, K., Zhang, C., Rui, X., Gan, D., & Li, W. (2025) · Food Reviews International · doi:10.1080/87559129.2025.2524405 · Cited by 30 · see citing works
The Hydroponic Rockwool Root Microbiome: Under Control or Underutilised?
Thomas, P. E. J., Knox, O. G. G., Powell, J. R., SINDEL, B. M., & Winter, G. (2023) · Microorganisms · doi:10.3390/microorganisms11040835 · Cited by 27 · see citing works
Zhang, Z., Femi-Oyetoro, J., Fidan, I., Ismail, M., & Allen, M. J. (2021) · Metals · doi:10.3390/met11050690 · Cited by 27 · see citing works
Microbiome analysis: An emerging forensic investigative tool
Kumari, P., Prakash, P., Yadav, S. K., & Saran, V. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111462 · Cited by 28 · see citing works
Piotrowska, U., Tsoi, J. K. H., Singh, P., Banerjee, A., & Sobczak, M. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5c01062 · Cited by 25 · see citing works
Current clinical translation of microbiome medicines
McCoubrey, L. E., Elbadawi, M., & Basit, A. W. (2022) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.02.001 · Cited by 25 · see citing works
Sadegh‐Zadeh, S., Sakha, H., Movahedi, S., Harandi, A. F., Ghaffari, S., Javanshir, E., Ali, S. A., Hooshanginezhad, Z., & Hajizadeh, R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107696 · Cited by 25 · see citing works
Therapeutic Modulation of Gut Microbiome in Cardiovascular Disease: A Literature Review
Suresh, M. G., Mohamed, S., Yukselen, Z., Hatwal, J., Venkatakrishnan, A., Metri, A. A., Bhardwaj, A., Singh, A., Bush, N., & Batta, A. (2024) · Heart and Mind · doi:10.4103/hm.hm-d-24-00044 · Cited by 26 · see citing works
- Authors
- Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., Gasbarrini, A., & Tortora, G.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- University College Cork (IE)
- Corresponding author
- Cammarota, Giovanni
- ORCID
- Venue
- Nature Reviews Gastroenterology & Hepatology
- Published
- 2020-07-09
- Volume
- 17
- Issue
- 10
- Pages
- 635-648
- ISSN
- 1759-5045 (ISSN-L)
- 1759-5053
- OpenAlex
- W3040925197
- Cited by
- 324
- References
- 250
- Citations by year
- 2026: 32 · 2025: 47 · 2024: 70 · 2023: 65 · 2022: 54 · 2021: 51 · 2020: 4 · 2014: 1
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Reviews Gastroenterology & Hepatology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Oncology › Colorectal Cancer Treatments and Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- gut microbiota, cancer, precision medicine, machine learning, big data, cancer therapy response, cancer immunotherapy
- MeSH terms
- Gastrointestinal Microbiome, Machine Learning, Big Data, Humans, Neoplasms, Translational Research, Biomedical, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review