The Reactome pathway Knowledgebase

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395

Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes

Initiative, A. P. C. G., Biankin, A. V., Waddell, N., Kassahn, K. S., Gingras, M.-C., Muthuswamy, L. B., Johns, A. L., Miller, D. K., Wilson, P. J., Patch, A.-M., et al. (2012) · Nature · doi:10.1038/nature11547 · Cited by 2,058

The BioPAX community standard for pathway data sharing

Demir, E., Cary, M. P., Paley, S., Fukuda, K., Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., Luciano, J., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1666 · Cited by 745

LibSBML: an API Library for SBML

Bornstein, B. J., Keating, S. M., Jouraku, A., & Hucka, M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn051 · Cited by 373

Controlled vocabularies and semantics in systems biology

Courtot, M., Juty, N., Knüpfer, C., Waltemath, D., Zhukova, A., Dräger, A., Dumontier, M., Finney, A., Golebiewski, M., Hastings, J., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.77 · Cited by 316

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019) · Nature Communications · doi:10.1038/s41467-019-09234-6 · Cited by 14,611

STRING v10: protein–protein interaction networks, integrated over the tree of life

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436

The Gene Ontology Resource: 20 years and still GOing strong

The Gene Ontology Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1055 · Cited by 4,747

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., & Vilo, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz369 · Cited by 5,045

PubChem in 2021: new data content and improved web interfaces

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa971 · Cited by 3,953

HMDB 4.0: the human metabolome database for 2018

Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1089 · Cited by 3,438

Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946

A subcellular map of the human proteome

Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Björk, L., Breckels, L. M., et al. (2017) · Science · doi:10.1126/science.aal3321 · Cited by 2,813

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

Li, W., Xu, H., Xiao, T., Cong, L., Love, M. I., Zhang, F., Irizarry, R. A., Liu, J. S., Brown, M., & Liu, X. S. (2014) · Genome Biology · doi:10.1186/s13059-014-0554-4 · Cited by 2,684

Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma

Moffitt, R. A., Marayati, R., Flate, E. L., Volmar, K. E., Loeza, S. G. H., Hoadley, K. A., Rashid, N. U., Williams, L. A., Eaton, S. C., Chung, A. H., et al. (2015) · Nature Genetics · doi:10.1038/ng.3398 · Cited by 2,194

Current best practices in single-cell RNA-seq analysis: a tutorial

Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552

A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants

Fritsche, L. G., Igl, W., Bailey, J. N. C., Grassmann, F., Sengupta, S., Bragg-Gresham, J. L., Burdon, K. P., Hebbring, S. J., Wen, C., Gorski, M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3448 · Cited by 1,544

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

Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248

Gene expression elucidates functional impact of polygenic risk for schizophrenia

Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091

DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Pinero, J., Queralt-Rosinach, N., Bravo, A., Deu-Pons, J., Bauer-Mehren, A., Baron, M., Sanz, F., & Furlong, L. I. (2015) · Database · doi:10.1093/database/bav028 · Cited by 1,124

MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data

Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091

Identification of bioactive metabolites using activity metabolomics

Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0108-4 · Cited by 1,008

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

Javierre, B. M., Burren, O. S., Wilder, S. P., Kreuzhuber, R., Hill, S. M., Sewitz, S., Cairns, J., Wingett, S. W., Várnai, C., Thiecke, M. J., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.037 · Cited by 1,046

CD28 Costimulation: From Mechanism to Therapy

Esensten, J. H., Helou, Y. A., Chopra, G., Weiss, A., & Bluestone, J. A. (2016) · Immunity · doi:10.1016/j.immuni.2016.04.020 · Cited by 937

Undulating changes in human plasma proteome profiles across the lifespan

Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0673-2 · Cited by 981

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome

Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., et al. (2020) · doi:10.1101/2020.01.19.905109 · Cited by 960

RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types

Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y. Y., Carré, C., Burdin, N., Visan, L., Ceccarelli, M., Poidinger, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.041 · Cited by 958

Orchestrating single-cell analysis with Bioconductor

Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945

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

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Gillette, M. A., Satpathy, S., Cao, S., Dhanasekaran, S. M., Vasaikar, S. V., Krug, K., Petralia, F., Li, Y., Liang, W.-W., Reva, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.013 · Cited by 820

Metastatic Latency and Immune Evasion through Autocrine Inhibition of WNT

Malladi, S., Macalinao, D. G., Jin, X., He, L., Basnet, H., Zou, Y., de Stanchina, E., & Massagué, J. (2016) · Cell · doi:10.1016/j.cell.2016.02.025 · Cited by 791

TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge

Nugent, A. A., Lin, K., van Lengerich, B., Lianoglou, S., Przybyla, L., Davis, S. S., Llapashtica, C., Wang, J., Kim, D. J., Xia, D., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.007 · Cited by 745

Systems biology and multi-omics integration: viewpoints from the metabolomics research community

Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723

Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease

Olah, M., Menon, V., Habib, N., Taga, M. F., Ma, Y., Yung, C. J., Cimpean, M., Khairallah, A., Coronas-Samano, G., Sankowski, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19737-2 · Cited by 722

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Dimitrov, D., Türei, D., Garrido-Rodriguez, M., Burmedi, P. L., Nagai, J. S., Boys, C., Ramirez Flores, R. O., Kim, H., Szalai, B., Costa, I. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30755-0 · Cited by 664

Proteogenomic and metabolomic characterization of human glioblastoma

Wang, L.-B., Karpova, A., Gritsenko, M. A., Kyle, J. E., Cao, S., Li, Y., Rykunov, D., Colaprico, A., Rothstein, J. H., Hong, R., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.01.006 · Cited by 661

Genomics-driven precision medicine for advanced pancreatic cancer: early results from the COMPASS trial

Aung, K. L., Fischer, S. E., Denroche, R. E., Jang, G.-H., Dodd, A., Creighton, S., Southwood, B., Liang, S.-B., Chadwick, D., Zhang, A., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2994 · Cited by 618

Identification of Genetic Factors that Modify Clinical Onset of Huntington’s Disease

Lee, J.-M., Wheeler, V. C., Chao, M. J., Vonsattel, J. P. G., Pinto, R. M., Lucente, D., Abu-Elneel, K., Ramos, E. M., Mysore, J. S., Gillis, T., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.003 · Cited by 655

Multi-omic machine learning predictor of breast cancer therapy response

Sammut, S.-J., Crispin-Ortuzar, M., Chin, S.-F., Provenzano, E., Bardwell, H. A., Ma, W., Cope, W., Dariush, A., Dawson, S.-J., Abraham, J. E., et al. (2022) · Nature · doi:10.1038/s41586-021-04278-5 · Cited by 654

Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function

Shifrut, E., Carnevale, J., Tobin, V., Roth, T. L., Woo, J. M., Bui, C. T., Li, P. J., Diolaiti, M. E., Ashworth, A., & Marson, A. (2018) · Cell · doi:10.1016/j.cell.2018.10.024 · Cited by 573

Proteogenomic Characterization of Endometrial Carcinoma

Dou, Y., Kawaler, E. A., Cui Zhou, D., Gritsenko, M. A., Huang, C., Blumenberg, L., Karpova, A., Petyuk, V. A., Savage, S. R., Satpathy, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.026 · Cited by 485

Building a knowledge graph to enable precision medicine

Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

Mungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1128 · Cited by 441

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., Taskesen, E., Hammerschlag, A. R., Okbay, A., Zabaneh, D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3869 · Cited by 490

Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer

Tintelnot, J., Xu, Y., Lesker, T. R., Schönlein, M., Konczalla, L., Giannou, A. D., Pelczar, P., Kylies, D., Puelles, V. G., Bielecka, A. A., et al. (2023) · Nature · doi:10.1038/s41586-023-05728-y · Cited by 478

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

Biologically informed deep neural network for prostate cancer discovery

Elmarakeby, H. A., Hwang, J., Arafeh, R., Crowdis, J., Gang, S., Liu, D., AlDubayan, S. H., Salari, K., Kregel, S., Richter, C., et al. (2021) · Nature · doi:10.1038/s41586-021-03922-4 · Cited by 456

Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy

Chaffin, M., Papangeli, I., Simonson, B., Akkad, A.-D., Hill, M. C., Arduini, A., Fleming, S. J., Melanson, M., Hayat, S., Kost-Alimova, M., et al. (2022) · Nature · doi:10.1038/s41586-022-04817-8 · Cited by 418

The next-generation Open Targets Platform: reimagined, redesigned, rebuilt

Ochoa, D., Hercules, A., Carmona, M., Suveges, D., Baker, J., Malangone, C., Lopez, I., Miranda, A., Cruz-Castillo, C., Fumis, L., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1046 · Cited by 432

Regulatory T-cell and neutrophil extracellular trap interaction contributes to carcinogenesis in non-alcoholic steatohepatitis

Wang, H., Zhang, H., Wang, Y., Brown, Z. J., Xia, Y., Huang, Z., Shen, C., Hu, Z., Beane, J., Ansa-Addo, E. A., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.07.032 · Cited by 398

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

A comprehensive single cell transcriptional landscape of human hematopoietic progenitors

Pellin, D., Loperfido, M., Baricordi, C., Wolock, S. L., Montepeloso, A., Weinberg, O. K., Biffi, A., Klein, A. M., & Biasco, L. (2019) · Nature Communications · doi:10.1038/s41467-019-10291-0 · Cited by 360

Meningeal lymphatic dysfunction exacerbates traumatic brain injury pathogenesis.

Bolte, A. C., Dutta, A. B., Hurt, M. E., Smirnov, I., Kovacs, M. A., McKee, C. A., Ennerfelt, H. E., Shapiro, D., Nguyen, B. H., Frost, E. L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18113-4 · Cited by 357

Predicting transcriptional outcomes of novel multigene perturbations with GEARS

Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Huang, J. K., Carlin, D. E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P., & Ideker, T. (2018) · Cell Systems · doi:10.1016/j.cels.2018.03.001 · Cited by 289

WD40 repeat domain proteins: a novel target class?

Schapira, M., Tyers, M., Torrent, M., & Arrowsmith, C. H. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.179 · Cited by 335

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

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

Utilizing graph machine learning within drug discovery and development

Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

Hill, W. D., Marioni, R. E., Maghzian, O., Ritchie, S. J., Hagenaars, S. P., McIntosh, A. M., Gale, C. R., Davies, G., & Deary, I. J. (2019) · Molecular Psychiatry · doi:10.1038/s41380-017-0001-5 · Cited by 311

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

Germline Chd8 haploinsufficiency alters brain development in mouse

Gompers, A. L., Su-Feher, L., Ellegood, J., Copping, N. A., Riyadh, M. A., Stradleigh, T. W., Pride, M. C., Schaffler, M. D., Wade, A. A., Catta-Preta, R., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4592 · Cited by 266

Single-cell analysis of olfactory neurogenesis and differentiation in adult humans

Durante, M. A., Kurtenbach, S., Sargi, Z. B., Harbour, J. W., Choi, R., Kurtenbach, S., Goss, G. M., Matsunami, H., & Goldstein, B. J. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0587-9 · Cited by 254

Atypical KRASG12R Mutant Is Impaired in PI3K Signaling and Macropinocytosis in Pancreatic Cancer

Hobbs, G. A., Baker, N. M., Miermont, A. M., Thurman, R. D., Pierobon, M., Tran, T. H., Anderson, A. O., Waters, A. M., Diehl, J. N., Papke, B., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1006 · Cited by 231

SABIO-RK: an updated resource for manually curated biochemical reaction kinetics

Wittig, U., Rey, M., Weidemann, A., Kania, R., & Müller, W. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1065 · Cited by 204

The BRENDA enzyme information system–From a database to an expert system

Schomburg, I., Jeske, L., Ulbrich, M., Placzek, S., Chang, A., & Schomburg, D. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.04.020 · Cited by 222

Impact of CHO Metabolism on Cell Growth and Protein Production: An Overview of Toxic and Inhibiting Metabolites and Nutrients

Pereira, S., Kildegaard, H. F., & Andersen, M. R. (2018) · Biotechnology Journal · doi:10.1002/biot.201700499 · Cited by 214

HumanNet v2: human gene networks for disease research

Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M., & Lee, I. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1126 · Cited by 210

Trem2 promotes anti-inflammatory responses in microglia and is suppressed under pro-inflammatory conditions

Liu, W., Taso, O., Wang, R., Bayram, S., Graham, A. C., Garcia-Reitboeck, P., Mallach, A., Andrews, W. D., Piers, T. M., Botia, J. A., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa209 · Cited by 214

Probiotic Bacillus subtilis protects against α-synuclein aggregation in C. elegans

Goya, M. E., Xue, F., Sampedro-Torres-Quevedo, C., Arnaouteli, S., Riquelme-Dominguez, L., Romanowski, A., Brydon, J., Ball, K. L., Stanley-Wall, N. R., & Doitsidou, M. (2020) · Cell Reports · doi:10.1016/j.celrep.2019.12.078 · Cited by 205

Determination of host proteins composing the microenvironment of coronavirus replicase complexes by proximity-labeling

V'kovski, P., Gerber, M., Kelly, J., Pfaender, S., Ebert, N., Braga Lagache, S., Simillion, C., Portmann, J., Stalder, H., Gaschen, V., et al. (2019) · eLife · doi:10.7554/elife.42037 · Cited by 186

A Review of bioinformatics tools for bio-prospecting from metagenomic sequence data

Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00023 · Cited by 199

The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping

Beresford-Jones, B. S., Forster, S. C., Stares, M. D., Notley, G., Viciani, E., Browne, H. P., Boehmler, D. J., Soderholm, A. T., Kumar, N., Vervier, K., et al. (2022) · Cell Host & Microbe · doi:10.1016/j.chom.2021.12.003 · Cited by 194

Single cell molecular alterations reveal target cells and pathways of concussive brain injury

Arneson, D., Zhang, G., Ying, Z., Zhuang, Y., Byun, H. R., Ahn, I. S., Gomez-Pinilla, F., & Yang, X. (2018) · Nature Communications · doi:10.1038/s41467-018-06222-0 · Cited by 193

Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies

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Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq

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MetaNetX/MNXref–reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks

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Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age

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TCRD and pharos 2021: mining the human proteome for disease biology

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Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

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GWAS for executive function and processing speed suggests involvement of the CADM2 gene

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Beta-Poisson model for single-cell RNA-seq data analyses

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Overcoming Genetically Based Resistance Mechanisms to PD-1 Blockade

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Improving the diagnostic yield of exome- sequencing by predicting gene–phenotype associations using large-scale gene expression analysis

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Development of an in vitro human liver system for interrogating nonalcoholic steatohepatitis

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The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials

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The BLUEPRINT Data Analysis Portal

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A human multi-lineage hepatic organoid model for liver fibrosis

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Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease

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Transcriptional and functional insights into the host immune response against the emerging fungal pathogen Candida auris

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The Mosaic Genome of Indigenous African Cattle as a Unique Genetic Resource for African Pastoralism

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Exploration of databases and methods supporting drug repurposing: a comprehensive survey

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CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

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Pharos 2023: an integrated resource for the understudied human proteome

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Aging and sarcopenia associate with specific interactions between gut microbes, serum biomarkers and host physiology in rats

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Towards a standard diet-induced and biopsy-confirmed mouse model of non-alcoholic steatohepatitis: Impact of dietary fat source

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Machete identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis

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Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker

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Exercise-Induced Activated Platelets Increase Adult Hippocampal Precursor Proliferation and Promote Neuronal Differentiation

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Mistimed food intake and sleep alters 24-hour time-of-day patterns of the human plasma proteome

Depner, C. M., Melanson, E. L., McHill, A. W., & Wright, K. P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714813115 · Cited by 122

VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics

Seninge, L., Anastopoulos, I., Ding, H., & Stuart, J. (2021) · Nature Communications · doi:10.1038/s41467-021-26017-0 · Cited by 107

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

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