Gut microbiome, big data and machine learning to promote precision medicine for cancer

Search and clustering orders of magnitude faster than BLAST

Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 21,098

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

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

The human microbiome project

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

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

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

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

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

Machine Learning in Medicine

Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579

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

Commensal Bifidobacterium 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

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

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 3,400

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

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

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

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

Fusobacterium nucleatum Potentiates Intestinal Tumorigenesis and Modulates the Tumor-Immune Microenvironment

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

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

Fusobacterium nucleatum Promotes Colorectal Carcinogenesis by Modulating E-Cadherin/β-Catenin Signaling via its FadA Adhesin

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

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

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

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

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

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

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

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

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

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

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

Genomic analysis identifies association of Fusobacterium 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

MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data

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 1,946

Activation of the NLRP3 inflammasome in dendritic cells induces IL-1β–dependent adaptive immunity against tumors

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

A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses

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

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

Artificial 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

The microbiome and cancer

Schwabe, R. F., & Jobin, C. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3610 · Cited by 1,689

Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug

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

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

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

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 1,530

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

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

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

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

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

Cancer and the microbiota

Garrett, W. S. (2015) · Science · doi:10.1126/science.aaa4972 · Cited by 1,404

The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression

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

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

Human gut microbiome: Hopes, threats and promises

Cani, P. D. (2018) · Gut · doi:10.1136/gutjnl-2018-316723 · Cited by 1,386

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

Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer

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

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

Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies

Wang, Y., & Qian, P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007401 · Cited by 1,333

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 1,288

Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer

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 1,273

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

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

Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation

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

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 1,012

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

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

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

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

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

Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer

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

Enterococcus hirae and Barnesiella intestinihominis Facilitate Cyclophosphamide-Induced Therapeutic Immunomodulatory Effects

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

Microbiota: a key orchestrator of cancer therapy

Roy, S., & Trinchieri, G. (2017) · Nature Reviews Cancer · doi:10.1038/nrc.2017.13 · Cited by 883

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 859

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 782

Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma

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 782

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

Oxidative stress and apoptosis: Impact on cancer therapy

Ozben, T. (2007) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20874 · Cited by 751

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

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

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 731

Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients

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

Predicting and Manipulating Cardiac Drug Inactivation by the Human Gut Bacterium Eggerthella lenta

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

Machine learning for integrating data in biology and medicine: principles, practice, and opportunities

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

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

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

The Gut Microbiome Modulates Colon Tumorigenesis

Zackular, J. P., Baxter, N. T., Iverson, K. D., Sadler, W. D., Petrosino, J. F., Chen, G. Y., & Schloss, P. D. (2013) · mBio · doi:10.1128/mbio.00692-13 · Cited by 629

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 604

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

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

The Diversity of Gut Microbiome is Associated With Favorable Responses to Anti–Programmed Death 1 Immunotherapy in Chinese Patients With NSCLC

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

Aspects of Gut Microbiota and Immune System Interactions in Infectious Diseases, Immunopathology, and Cancer

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 552

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

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

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 536

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 494

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

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

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 454

Gut microbiome function predicts response to anti-integrin biologic therapy in Inflammatory Bowel diseases

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

Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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

Systematic review: the role of the gut microbiota in chemotherapy‐ or radiation‐induced gastrointestinal mucositis – current evidence and potential clinical applications

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 437

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 432

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 428

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

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

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

Lactobacillus supplementation for diarrhoea related to chemotherapy of colorectal cancer: a randomised study

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 413

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 400

The Host Microbiome Regulates and Maintains Human Health: A Primer and Perspective for Non-Microbiologists

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 400

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

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 340

High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia

Drewes, 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

Radiation induces proinflammatory dysbiosis: transmission of inflammatory susceptibility by host cytokine induction

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 331

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 325

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

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

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

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 296

Potential for enriching next-generation health-promoting gut bacteria through prebiotics and other dietary components

Lordan, C., Thapa, D., Ross, R. P., & Cotter, P. D. (2020) · Gut Microbes · doi:10.1080/19490976.2019.1613124 · Cited by 289

International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis

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

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 279

Randomized controlled trial of live lactobacillus acidophilus plus bifidobacterium bifidum in prophylaxis of diarrhea during radiotherapy in cervical cancer patients

Chitapanarux, I., Chitapanarux, T., Traisathit, P., Kudumpee, S., Tharavichitkul, E., & Lorvidhaya, V. (2010) · Radiation Oncology · doi:10.1186/1748-717x-5-31 · Cited by 266

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 264

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 245

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 240

Prevention of irinotecan induced diarrhea by probiotics: A randomized double blind, placebo controlled pilot study

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

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 235

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

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

Alanazi, H. O., Abdullah, A. H., & Qureshi, K. N. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0715-6 · Cited by 231

Gut Microbial Dysbiosis May Predict Diarrhea and Fatigue in Patients Undergoing Pelvic Cancer Radiotherapy: A Pilot Study

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 230

Impact of Pelvic Radiotherapy on Gut Microbiota of Gynecological Cancer Patients Revealed by Massive Pyrosequencing

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

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

The cancer microbiome

Elinav, E., Garrett, W. S., Trinchieri, G., & Wargo, J. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0155-3 · Cited by 215

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

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

Microbiota- and Radiotherapy-Induced Gastrointestinal Side-Effects (MARS) Study: A Large Pilot Study of the Microbiome in Acute and Late-Radiation Enteropathy

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 205

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 202

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

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 198

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 198

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

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 191

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers

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

Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer

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

The challenges of integrating multi-omic data sets

Palsson, B., & Zengler, K. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.462 · Cited by 178

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

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 176

Predictable modulation of cancer treatment outcomes by the gut microbiota

(2020) · doi:10.1186/s40168-020-00811-2 · Cited by 175

Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network

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

A Metagenomic Meta-analysis Reveals Functional Signatures of Health and Disease in the Human Gut Microbiome

Armour, C. R., Nayfach, S., Pollard, K. S., & Sharpton, T. J. (2019) · mSystems · doi:10.1128/msystems.00332-18 · Cited by 163

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

The Microbiome and Radiation Induced-Bowel Injury: Evidence for Potential Mechanistic Role in Disease Pathogenesis

Kumagai, T., Rahman, F. Z., & Smith, A. M. (2018) · Nutrients · doi:10.3390/nu10101405 · Cited by 162

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

Linking Gut Microbiota to Colorectal Cancer

Raskov, H., Burcharth, J., & Pommergaard, H. (2017) · Journal of Cancer · doi:10.7150/jca.20497 · Cited by 158

Effects of the enteral administration of Bifidobacterium breve on patients undergoing chemotherapy for pediatric malignancies

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 156

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

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

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

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition

Thompson, J. C., Johansen, R., Dunbar, J., & Munsky, B. E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215502 · Cited by 142

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 140

Composition of the mucosa‐associated microbiota along the entire gastrointestinal tract of human individuals

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

Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma

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 136

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

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

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 128

Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis

Lui, T. K.-L., Guo, C., & Leung, W. K. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.02.033 · Cited by 127

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation

Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

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 125

Lessons Learned from the Irinotecan Metabolic Pathway

Mader, W. F., & McLeod, H. L. (2003) · Current Medicinal Chemistry · doi:10.2174/0929867033368619 · Cited by 122

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 122

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies

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 121

MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction

LaPierre, N., Ju, C. J. ‐., Zhou, G., & Wang, W. W. (2019) · Methods · doi:10.1016/j.ymeth.2019.03.003 · Cited by 118

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

Gut dysbiosis, leaky gut, and intestinal epithelial proliferation in neurological disorders: towards the development of a new therapeutic using amino acids, prebiotics, probiotics, and postbiotics

Maguire, M., & Maguire, G. (2018) · Reviews in the Neurosciences · doi:10.1515/revneuro-2018-0024 · Cited by 114

Probiotics for the prevention or treatment of chemotherapy- or radiotherapy-related diarrhoea in people with cancer

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

Probiotics for prevention of radiation-induced diarrhea: A meta-analysis of randomized controlled trials

Liu, M.-M., Li, S., Shu, Y., & Zhan, H.-Q. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178870 · Cited by 107

Probiotic Supplementation Decreases Chemotherapy-induced Gastrointestinal Side Effects in Patients With Acute Leukemia

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

Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults

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 105

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 103

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 99

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 98

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 98

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 98

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

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 95

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 94

Computer-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

Application of a neural network whole transcriptome-based pan-cancer method for diagnosis of primary and metastatic cancers

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

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 89

Inflammatory Bowel Disease: A Potential Result from the Collusion between Gut Microbiota and Mucosal Immune System

Yue, B., Luo, X., Yu, Z., Mani, S., Wang, Z., & Dou, W. (2019) · Microorganisms · doi:10.3390/microorganisms7100440 · Cited by 86

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 85

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

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

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

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

Gut microbiota composition after diet and probiotics in overweight breast cancer survivors: a randomized open-label pilot intervention trial

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 79

The efficacy and safety of probiotics for prevention of chemoradiotherapy-induced diarrhea in people with abdominal and pelvic cancer: a systematic review and meta-analysis

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

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

Impact of the microbiome on checkpoint inhibitor treatment in patients with non-small cell lung cancer and melanoma

Shaikh, F. Y., Gills, J. J., & Sears, C. L. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.076 · Cited by 76

Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods

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

Microbiome Learning Repo (ML Repo): A public repository of microbiome regression and classification tasks

Vangay, P., Hillmann, B., & Knights, D. (2019) · GigaScience · doi:10.1093/gigascience/giz042 · Cited by 73

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

Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study

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 70

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 66

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

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

An unsupervised learning approach to identify novel signatures of health and disease from multimodal data

Shomorony, I., Cirulli, E. T., Huang, L., Napier, L. A., Heister, R., Hicks, M. A., Cohen, I. V., Yu, H., Swisher, C. L., Schenker-Ahmed, N. M., et al. (2020) · Genome Medicine · doi:10.1186/s13073-019-0705-z · Cited by 61

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

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

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 510

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 494

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 320

The influence of machine learning technologies in gut microbiome research and cancer studies - A review

Loganathan, T., & Doss, C. G. P. (2022) · Life Sciences · doi:10.1016/j.lfs.2022.121118 · Cited by 20

Gut Microbiome and Precision Medicine in Cancer

Mbodi, L., Chatziioannou, A. A., & Dlamini, Z. (2024) · CRC Press eBooks · doi:10.1201/9781032706450-14

Host Microbiomes in Tumor Precision Medicine: How far are we?

D’Amico, F., Barone, M., Tavella, T., Rampelli, S., Brigidi, P., & Turroni, S. (2022) · Current Medicinal Chemistry · doi:10.2174/0929867329666220105121754 · Cited by 16

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Feng, J., Yang, K., Liu, X., Song, M. S., Zhan, P., Zhang, M., Chen, J., & Liu, J. (2023) · PeerJ · doi:10.7717/peerj.16304 · Cited by 4

Artificial Intelligence Application to Microbiomics Data for Improved Clinical Decision Making in Precision Oncology

Mbatha, S. Z., Mkhize‐Kwitshana, Z. L., Mulaudzi, T. V., Hull, R., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_8 · Cited by 1