KEGG: Kyoto Encyclopedia of Genes and Genomes

Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity

Henegar, C., Tordjman, J., Achard, V., Lacasa, D., Cremer, I., Guerre-Millo, M., Poitou, C., Basdevant, A., Stich, V., Viguerie, N., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-1-r14 · Cited by 471

NAD + supplementation normalizes key Alzheimer’s features and DNA damage responses in a new AD mouse model with introduced DNA repair deficiency

Hou, Y., Lautrup, S., Cordonnier, S., Wang, Y., Croteau, D. L., Zavala, E., Zhang, Y., Moritoh, K., O’Connell, J. F., Baptiste, B. A., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718819115 · Cited by 472

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

A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks

Parnas, O., Jovanovic, M., Eisenhaure, T. M., Herbst, R. H., Dixit, A., Ye, C. J., Przybylski, D., Platt, R. J., Tirosh, I., Sanjana, N. E., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.059 · Cited by 440

An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography

Nayfach, S., Rodriguez-Mueller, B., Garud, N., & Pollard, K. S. (2016) · Genome Research · doi:10.1101/gr.201863.115 · Cited by 470

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Sun, Y. V., & Hu, Y.-J. (2016) · Advances in Genetics · doi:10.1016/bs.adgen.2015.11.004 · Cited by 459

De novo assembly and characterization of root transcriptome using Illumina paired-end sequencing and development of cSSR markers in sweetpotato (Ipomoea batatas)

Wang, Z., Fang, B., Chen, J., Zhang, X., Luo, Z., Huang, L., Chen, X., & Li, Y. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-726 · Cited by 433

An atlas of human metabolism

Robinson, J. L., Kocabaş, P., Wang, H., Cholley, P.-E., Cook, D., Nilsson, A., Anton, M., Ferreira, R., Domenzain, I., Billa, V., et al. (2020) · Science Signaling · doi:10.1126/scisignal.aaz1482 · Cited by 442

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Rossin, E. J., Lage, K., Raychaudhuri, S., Xavier, R. J., Tatar, D., Benita, Y., Constortium, I. I. B. D. G., Cotsapas, C., & Daly, M. J. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001273 · Cited by 438

Effects of microbiota-directed foods in gnotobiotic animals and undernourished children

Gehrig, J. L., Venkatesh, S., Chang, H.-W., Hibberd, M. C., Kung, V. L., Cheng, J., Chen, R. Y., Subramanian, S., Cowardin, C. A., Meier, M. F., et al. (2019) · Science · doi:10.1126/science.aau4732 · Cited by 431

Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S.-F., Schwarz, R. F., Curtis, C., Dunning, M. J., Bardwell, H., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3004330 · Cited by 426

The microbiota regulate neuronal function and fear extinction learning

Chu, C., Murdock, M. H., Jing, D., Won, T. H., Chung, H., Kressel, A. M., Tsaava, T., Addorisio, M. E., Putzel, G. G., Zhou, L., et al. (2019) · Nature · doi:10.1038/s41586-019-1644-y · Cited by 421

Faecalibacterium prausnitzii subspecies-level dysbiosis in the human gut microbiome underlying atopic dermatitis

Song, H., Yoo, Y., Hwang, J., Na, Y.-C., & Kim, H. S. (2016) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2015.08.021 · Cited by 416

Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome

Shi, W., Moon, C. D., Leahy, S. C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., et al. (2014) · Genome Research · doi:10.1101/gr.168245.113 · Cited by 414

Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears

Liu, S., Lorenzen, E. D., Fumagalli, M., Li, B., Harris, K., Xiong, Z., Zhou, L., Korneliussen, T. S., Somel, M., Babbitt, C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.03.054 · Cited by 411

Deep Sequencing of the Oral Microbiome Reveals Signatures of Periodontal Disease

Liu, B., Faller, L. L., Klitgord, N., Mazumdar, V., Ghodsi, M., Sommer, D. D., Gibbons, T. R., Treangen, T. J., Chang, Y.-C., Li, S., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037919 · Cited by 411

Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways

the Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Der, E., Suryawanshi, H., Morozov, P., Kustagi, M., Goilav, B., Ranabothu, S., Izmirly, P., Clancy, R., Belmont, H. M., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0386-1 · Cited by 403

Critical pathways in cellular senescence and immortalization revealed by gene expression profiling

Fridman, A. L., & Tainsky, M. A. (2008) · Oncogene · doi:10.1038/onc.2008.213 · Cited by 385

Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins

Hu, P., Janga, S. C., Babu, M., Díaz-Mejía, J. J., Butland, G., Yang, W., Pogoutse, O., Guo, X., Phanse, S., Wong, P., et al. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000096 · Cited by 387

Members of the human gut microbiota involved in recovery from Vibrio cholerae infection

Hsiao, A., Ahmed, A. M. S., Subramanian, S., Griffin, N. W., Drewry, L. L., Petri, W. A., Haque, R., Ahmed, T., & Gordon, J. I. (2014) · Nature · doi:10.1038/nature13738 · Cited by 387

Metatranscriptomics of the Human Oral Microbiome during Health and Disease

Jorth, P., Turner, K. H., Gumus, P., Nizam, N., Buduneli, N., & Whiteley, M. (2014) · mBio · doi:10.1128/mbio.01012-14 · Cited by 386

Recommendations for the design and analysis of epigenome-wide association studies

Michels, K. B., Binder, A. M., Dedeurwaerder, S., Epstein, C. B., Greally, J. M., Gut, I., Houseman, E. A., Izzi, B., Kelsey, K. T., Meissner, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2632 · Cited by 378

Peripheral blood mononuclear cell gene expression profiles predict poor outcome in idiopathic pulmonary fibrosis

Herazo-Maya, J. D., Noth, I., Duncan, S. R., Kim, S., Ma, S.-F., Tseng, G. C., Feingold, E., Juan-Guardela, B. M., Richards, T. J., Lussier, Y., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005964 · Cited by 325

Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia

Tam, O. H., Rozhkov, N. V., Shaw, R., Kim, D., Hubbard, I., Fennessey, S., Propp, N., Phatnani, H., Kwan, J., Sareen, D., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.066 · Cited by 327

Structural variation in the gut microbiome associates with host health

Zeevi, D., Korem, T., Godneva, A., Bar, N., Kurilshikov, A., Lotan-Pompan, M., Weinberger, A., Fu, J., Wijmenga, C., Zhernakova, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1065-y · Cited by 351

Optical Pooled Screens in Human Cells

Feldman, D., Singh, A., Schmid-Burgk, J. L., Carlson, R. J., Mezger, A., Garrity, A. J., Zhang, F., & Blainey, P. C. (2019) · Cell · doi:10.1016/j.cell.2019.09.016 · Cited by 321

Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy

Smith, M., Dai, A., Ghilardi, G., Amelsberg, K. V., Devlin, S. M., Pajarillo, R., Slingerland, J. B., Beghi, S., Herrera, P. S., Giardina, P., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01702-9 · Cited by 349

A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

Brylinski, M., & Skolnick, J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707684105 · Cited by 333

Human skeletal muscle possesses an epigenetic memory of hypertrophy

Seaborne, R. A., Strauss, J., Cocks, M., Shepherd, S., O’Brien, T. D., van Someren, K. A., Bell, P. G., Murgatroyd, C., Morton, J. P., Stewart, C. E., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-20287-3 · Cited by 334

Index and biological spectrum of human DNase I hypersensitive sites

Meuleman, W., Muratov, A., Rynes, E., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Teodosiadis, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2559-3 · Cited by 331

Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome

Maier, T. V., Lucio, M., Lee, L. H., VerBerkmoes, N. C., Brislawn, C. J., Bernhardt, J., Lamendella, R., McDermott, J. E., Bergeron, N., Heinzmann, S. S., et al. (2017) · mBio · doi:10.1128/mbio.01343-17 · Cited by 316

Chronic stress-induced gut dysfunction exacerbates Parkinson's disease phenotype and pathology in a rotenone-induced mouse model of Parkinson's disease

Dodiya, H. B., Forsyth, C. B., Voigt, R. M., Engen, P. A., Patel, J., Shaikh, M., Green, S. J., Naqib, A., Roy, A., Kordower, J. H., et al. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2018.12.012 · Cited by 305

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

Reguly, T., Breitkreutz, A., Boucher, L., Breitkreutz, B.-J., Hon, G. C., Myers, C. L., Parsons, A., Friesen, H., Oughtred, R., Tong, A., et al. (2006) · Journal of Biology · doi:10.1186/jbiol36 · Cited by 326

Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses

Korem, T., Zeevi, D., Zmora, N., Weissbrod, O., Bar, N., Lotan-Pompan, M., Avnit-Sagi, T., Kosower, N., Malka, G., Rein, M., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.05.002 · Cited by 318

Single-cell transcriptional profiles in human skeletal muscle

Rubenstein, A. B., Smith, G. R., Raue, U., Begue, G., Minchev, K., Ruf-Zamojski, F., Nair, V. D., Wang, X., Zhou, L., Zaslavsky, E., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-57110-6 · Cited by 323

Microbial metabolites control the thymic development of mucosal-associated invariant T cells

Legoux, F., Bellet, D., Daviaud, C., El Morr, Y., Darbois, A., Niort, K., Procopio, E., Salou, M., Gilet, J., Ryffel, B., et al. (2019) · Science · doi:10.1126/science.aaw2719 · Cited by 320

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Cano-Gamez, K., Soskic, B., Roumeliotis, T. I., So, E., Smyth, D. J., Baldrighi, M., Willé, D., Nakic, N., Esparza-Gordillo, J., Larminie, C. G. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15543-y · Cited by 274

Novel Entries in a Fungal Biofilm Matrix Encyclopedia

Zarnowski, R., Westler, W. M., Lacmbouh, G. A., Marita, J. M., Bothe, J. R., Bernhardt, J., Lounes-Hadj Sahraoui, A., Fontaine, J., Sanchez, H., Hatfield, R. D., et al. (2014) · mBio · doi:10.1128/mbio.01333-14 · Cited by 315

The signature of maternal rearing in the methylome in rhesus macaque prefrontal cortex and T cells

Provençal, N., Suderman, M. J., Guillemin, C., Massart, R., Ruggiero, A., Wang, D., Bennett, A. J., Pierre, P. J., Friedman, D. P., Côté, S. M., et al. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.1470-12.2012 · Cited by 312

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Moss, D. J. H., Pardiñas, A. F., Langbehn, D., Lo, K., Leavitt, B. R., Roos, R., Durr, A., Mead, S., Coleman, A., Santos, R. D., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30161-8 · Cited by 312

Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: A nontargeted metabolomic study

Snowden, S. G., Ebshiana, A. A., Hye, A., An, Y., Pletnikova, O., O’Brien, R., Troncoso, J., Legido-Quigley, C., & Thambisetty, M. (2017) · PLOS Medicine · doi:10.1371/journal.pmed.1002266 · Cited by 309

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282

Proteogenomic Characterization of Human Early-Onset Gastric Cancer

Mun, D.-G., Bhin, J., Kim, S., Kim, H., Jung, J. H., Jung, Y., Jang, Y. E., Park, J. M., Kim, H., Jung, Y., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2018.12.003 · Cited by 294

Birth mode is associated with earliest strain-conferred gut microbiome functions and immunostimulatory potential

Wampach, L., Heintz-Buschart, A., Fritz, J. V., Ramiro-Garcia, J., Habier, J., Herold, M., Narayanasamy, S., Kaysen, A., Hogan, A. H., Bindl, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07631-x · Cited by 292

Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets

Neff, R. A., Wang, M., Vatansever, S., Guo, L., Ming, C., Wang, Q., Wang, E., Horgusluoglu-Moloch, E., Song, W.-M., Li, A., et al. (2021) · Science Advances · doi:10.1126/sciadv.abb5398 · Cited by 306

Wnt-Pathway Activation in Two Molecular Classes of Hepatocellular Carcinoma and Experimental Modulation by Sorafenib

Lachenmayer, A., Alsinet, C., Savic, R., Cabellos, L., Toffanin, S., Hoshida, Y., Villanueva, A., Minguez, B., Newell, P., Tsai, H.-W., et al. (2012) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-11-2322 · Cited by 306

De novo sequencing and analysis of the American ginseng root transcriptome using a GS FLX Titanium platform to discover putative genes involved in ginsenoside biosynthesis

Sun, C., Li, Y., Wu, Q., Luo, H., Sun, Y., Song, J., Lui, E. M., & Chen, S. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-262 · Cited by 286

Time of exercise specifies the impact on muscle metabolic pathways and systemic energy homeostasis

Sato, S., Basse, A. L., Schönke, M., Chen, S., Samad, M., Altıntaş, A., Laker, R. C., Dalbram, E., Barrès, R., Baldi, P., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.03.013 · Cited by 298

Reconstructing organisms in silico: genome-scale models and their emerging applications

Fang, X., Lloyd, C. J., & Palsson, B. O. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-00440-4 · Cited by 292

Diverse Genome-wide Association Studies Associate the IL12/IL23 Pathway with Crohn Disease

Wang, K., Zhang, H., Kugathasan, S., Annese, V., Bradfield, J. P., Russell, R. K., Sleiman, P. M. A., Imielinski, M., Glessner, J., Hou, C., et al. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.01.026 · Cited by 291

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

(DeCOI), G. C.-1. O. I., Aschenbrenner, A. C., Mouktaroudi, M., Krämer, B., Oestreich, M., Antonakos, N., Nuesch-Germano, M., Gkizeli, K., Bonaguro, L., Reusch, N., et al. (2021) · Genome Medicine · doi:10.1186/s13073-020-00823-5 · Cited by 255

Inhibition of mTOR induces a paused pluripotent state

Bulut-Karslioglu, A., Biechele, S., Jin, H., Macrae, T. A., Hejna, M., Gertsenstein, M., Song, J. S., & Ramalho-Santos, M. (2016) · Nature · doi:10.1038/nature20578 · Cited by 283

Candida albicans biofilm–induced vesicles confer drug resistance through matrix biogenesis

Zarnowski, R., Sanchez, H., Covelli, A. S., Dominguez, E., Jaromin, A., Bernhardt, J., Mitchell, K. F., Heiss, C., Azadi, P., Mitchell, A., et al. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2006872 · Cited by 260

Mesenchymal stem cell-derived exosomes: a new therapeutic approach to osteoarthritis?

Mianehsaz, E., Mirzaei, H. R., Mahjoubin-Tehran, M., Rezaee, A., Sahebnasagh, R., Pourhanifeh, M. H., Mirzaei, H., & Hamblin, M. R. (2019) · Stem Cell Research & Therapy · doi:10.1186/s13287-019-1445-0 · Cited by 274

In silico method for modelling metabolism and gene product expression at genome scale

Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., Schrimpe-Rutledge, A. C., Smith, R. D., Adkins, J. N., Zengler, K., et al. (2012) · Nature Communications · doi:10.1038/ncomms1928 · Cited by 266

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

Bosi, E., Monk, J. M., Aziz, R. K., Fondi, M., Nizet, V., & Palsson, B. Ø. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1523199113 · Cited by 266

Genomic diversity and evolution of the head crest in the rock pigeon

Shapiro, M. D., Kronenberg, Z., Li, C., Domyan, E. T., Pan, H., Campbell, M., Tan, H., Huff, C. D., Hu, H., Vickrey, A. I., et al. (2013) · Science · doi:10.1126/science.1230422 · Cited by 265

Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome

Bikel, S., Valdez-Lara, A., Cornejo-Granados, F., Rico, K., Canizales-Quinteros, S., Soberón, X., Del Pozo-Yauner, L., & Ochoa-Leyva, A. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.06.001 · Cited by 263

TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach

Zoppoli, P., Morganella, S., & Ceccarelli, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-154 · Cited by 248

Systematic changes in gene expression in postmortem human brains associated with tissue pH and terminal medical conditions

Li, J. Z., Vawter, M. P., Walsh, D. M., Tomita, H., Evans, S. J., Choudary, P. V., Lopez, J. F., Avelar, A., Shokoohi, V., Chung, T., et al. (2004) · Human Molecular Genetics · doi:10.1093/hmg/ddh065 · Cited by 261

Prolonged hypernutrition impairs TREM2-dependent efferocytosis to license chronic liver inflammation and NASH development

Wang, X., He, Q., Zhou, C., Xu, Y., Liu, D., Fujiwara, N., Kubota, N., Click, A., Henderson, P., Vancil, J., et al. (2023) · Immunity · doi:10.1016/j.immuni.2022.11.013 · Cited by 262

Bacteria Boost Mammalian Host NAD Metabolism by Engaging the Deamidated Biosynthesis Pathway

Shats, I., Williams, J. G., Liu, J., Makarov, M. V., Wu, X., Lih, F. B., Deterding, L. J., Lim, C., Xu, X., Randall, T. A., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.02.001 · Cited by 242

A Systems Biology Approach for the Investigation of the Heparin/Heparan Sulfate Interactome

Ori, A., Wilkinson, M. C., & Fernig, D. G. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.228114 · Cited by 251

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers

Ballouz, S., Verleyen, W., & Gillis, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv118 · Cited by 215

Human Cancer Protein-Protein Interaction Network: A Structural Perspective

Kar, G., Gursoy, A., & Keskin, O. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000601 · Cited by 235

Blood microRNA changes in depressed patients during antidepressant treatment

Bocchio-Chiavetto, L., Maffioletti, E., Bettinsoli, P., Giovannini, C., Bignotti, S., Tardito, D., Corrada, D., Milanesi, L., & Gennarelli, M. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.06.013 · Cited by 238

Variation in Rural African Gut Microbiota Is Strongly Correlated with Colonization by Entamoeba and Subsistence

Morton, E. R., Lynch, J., Froment, A., Lafosse, S., Heyer, E., Przeworski, M., Blekhman, R., & Ségurel, L. (2015) · PLOS Genetics · doi:10.1371/journal.pgen.1005658 · Cited by 231

New applications of synthetic biology tools for cyanobacterial metabolic engineering

Santos-Merino, M., Singh, A. K., & Ducat, D. C. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00033 · Cited by 222

MyCompoundID: using an evidence-based metabolome library for metabolite identification

Li, L., Li, R., Zhou, J., Zuniga, A., Stanislaus, A. E., Wu, Y., Huan, T., Zheng, J., Shi, Y., Wishart, D. S., et al. (2013) · Analytical Chemistry · doi:10.1021/ac400099b · Cited by 229

Toward better drug discovery with knowledge graph

Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.09.003 · Cited by 229

Genome-Wide Meta-Analysis Identifies Regions on 7p21 (AHR) and 15q24 (CYP1A2) As Determinants of Habitual Caffeine Consumption

Cornelis, M. C., Monda, K. L., Yu, K., Paynter, N., Azzato, E. M., Bennett, S. N., Berndt, S. I., Boerwinkle, E., Chanock, S., Chatterjee, N., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002033 · Cited by 228

Remnants of an Ancient Metabolism without Phosphate

Goldford, J. E., Hartman, H., Smith, T. F., & Segrè, D. (2017) · Cell · doi:10.1016/j.cell.2017.02.001 · Cited by 226

Biological aging and the human gut microbiota

Maffei, V. J., Kim, S., Blanchard, E., Luo, M., Jazwinski, S. M., Taylor, C. M., & Welsh, D. A. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx042 · Cited by 219

Guild-based analysis for understanding gut microbiome in human health and diseases

Wu, G., Zhao, N., Zhang, C., Lam, Y. Y., & Zhao, L. (2021) · Genome Medicine · doi:10.1186/s13073-021-00840-y · Cited by 203

MSNovelist: de novo structure generation from mass spectra

Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226

Diverse human extracellular RNAs are widely detected in human plasma

Freedman, J. E., Gerstein, M., Mick, E., Rozowsky, J., Levy, D., Kitchen, R., Das, S., Shah, R., Danielson, K., Beaulieu, L., et al. (2016) · Nature Communications · doi:10.1038/ncomms11106 · Cited by 202

Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control

Osorio, D., & Cai, J. J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa751 · Cited by 212

Neuronal MicroRNA Deregulation in Response to Alzheimer's Disease Amyloid-β

Schonrock, N., Ke, Y. D., Humphreys, D., Staufenbiel, M., Ittner, L. M., Preiss, T., & Götz, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011070 · Cited by 211

Transcriptional characterizations of differences between eutopic and ectopic endometrium

Wu, Y., Kajdacsy-Balla, A., Strawn, E., Basir, Z., Halverson, G., Jailwala, P., Wang, Y., Wang, X., Ghosh, S., & Guo, S.-W. (2006) · Endocrinology · doi:10.1210/en.2005-0426 · Cited by 208

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

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