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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets

Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

<i>Trem2</i> 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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 &amp; Microbe · doi:10.1016/j.chom.2021.12.003 · Cited by 194 · see citing works

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 · see citing works

Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies

Roth, T. L., Li, P. J., Blaeschke, F., Nies, J. F., Apathy, R., Mowery, C., Yu, R., Nguyen, M. L. T., Lee, Y., Truong, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.039 · Cited by 186 · see citing works

Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq

Song, Q., Hawkins, G. A., Wudel, L., Chou, P., Forbes, E., Pullikuth, A. K., Liu, L., Jin, G., Craddock, L., Topaloglu, U., et al. (2019) · Cancer Medicine · doi:10.1002/cam4.2113 · Cited by 168 · see citing works

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

Moretti, S., Martin, O., Van Du Tran, T., Bridge, A., Morgat, A., & Pagni, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1117 · Cited by 162 · see citing works

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

Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101070 · Cited by 166 · see citing works

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

Sheils, T. K., Mathias, S. L., Kelleher, K. J., Siramshetty, V. B., Nguyen, D.-T., Bologa, C. G., Jensen, L. J., Vidović, D., Koleti, A., Schürer, S. C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa993 · Cited by 160 · see citing works

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

Kenner, B., Chari, S. T., Kelsen, D., Klimstra, D. S., Pandol, S. J., Rosenthal, M., Rustgi, A. K., Taylor, J. A., Yala, A., Abul-Husn, N., et al. (2021) · Pancreas · doi:10.1097/mpa.0000000000001762 · Cited by 158 · see citing works

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

Ibrahim-Verbaas, C. A., Bressler, J., Debette, S., Schuur, M., Smith, A. V., Bis, J. C., Davies, G., Trompet, S., Smith, J. A., Wolf, C., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.37 · Cited by 147 · see citing works

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

Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M., & Pawitan, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw202 · Cited by 157 · see citing works

Overcoming Genetically Based Resistance Mechanisms to PD-1 Blockade

Torrejon, D. Y., Abril-Rodriguez, G., Champhekar, A. S., Tsoi, J., Campbell, K. M., Kalbasi, A., Parisi, G., Zaretsky, J. M., Garcia-Diaz, A., Puig-Saus, C., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1409 · Cited by 146 · see citing works

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

Deelen, P., van Dam, S., Herkert, J. C., Karjalainen, J. M., Brugge, H., Abbott, K. M., van Diemen, C. C., van der Zwaag, P. A., Gerkes, E. H., Zonneveld-Huijssoon, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10649-4 · Cited by 138 · see citing works

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

Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Pawson, A. J., Spedding, M., Davies, J. A., & NC-IUPHAR (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1010 · Cited by 149 · see citing works

The BLUEPRINT Data Analysis Portal

Fernández, J. M., de la Torre, V., Richardson, D., Royo, R., Puiggròs, M., Moncunill, V., Fragkogianni, S., Clarke, L., Flicek, P., Rico, D., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.10.021 · Cited by 151 · see citing works

A human multi-lineage hepatic organoid model for liver fibrosis

Guan, Y., Enejder, A., Wang, M., Fang, Z., Cui, L., Chen, S.-Y., Wang, J., Tan, Y., Wu, M., Chen, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26410-9 · Cited by 149 · see citing works

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

Winkler, T. W., Grassmann, F., Brandl, C., Kiel, C., Günther, F., Strunz, T., Weidner, L., Zimmermann, M. E., Korb, C. A., Poplawski, A., et al. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-00760-7 · Cited by 138 · see citing works

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

Bruno, M., Kersten, S., Bain, J. M., Jaeger, M., Rosati, D., Kruppa, M. D., Lowman, D. W., Rice, P. J., Graves, B., Ma, Z., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0780-3 · Cited by 145 · see citing works

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

Kim, K., Kwon, T., Dessie, T., Yoo, D., Mwai, O. A., Jang, J., Sung, S., Lee, S., Salim, B., Jung, J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0694-2 · Cited by 140 · see citing works

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

Tanoli, Z., Seemab, U., Scherer, A., Wennerberg, K., Tang, J., & Vähä-Koskela, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa003 · Cited by 131 · see citing works

Pharos 2023: an integrated resource for the understudied human proteome

Kelleher, K. J., Sheils, T. K., Mathias, S. L., Yang, J. J., Metzger, V. T., Siramshetty, V. B., Nguyen, D.-T., Jensen, L. J., Vidović, D., Schürer, S. C., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1033 · Cited by 137 · see citing works

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

Siddharth, J., Chakrabarti, A., Pannérec, A., Karaz, S., Morin-Rivron, D., Masoodi, M., Feige, J. N., & Parkinson, S. J. (2017) · Aging · doi:10.18632/aging.101262 · Cited by 127 · see citing works

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

Boland, M. L., Oró, D., Tølbøl, K. S., Thrane, S. T., Nielsen, J. C., Cohen, T. S., Tabor, D. E., Fernandes, F., Tovchigrechko, A., Veidal, S. S., et al. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i33.4904 · Cited by 129 · see citing works

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

Barriga, F. M., Tsanov, K. M., Ho, Y.-J., Sohail, N., Zhang, A., Baslan, T., Wuest, A. N., Del Priore, I., Meškauskaitė, B., Livshits, G., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00443-5 · Cited by 125 · see citing works

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

Duran-Frigola, M., Pauls, E., Guitart-Pla, O., Bertoni, M., Alcalde, V., Amat, D., Juan-Blanco, T., & Aloy, P. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0502-7 · Cited by 126 · see citing works

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

Leiter, O., Seidemann, S., Overall, R. W., Ramasz, B., Rund, N., Schallenberg, S., Grinenko, T., Wielockx, B., Kempermann, G., & Walker, T. L. (2019) · Stem Cell Reports · doi:10.1016/j.stemcr.2019.02.009 · Cited by 119 · see citing works

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 · see citing works

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 · see citing works

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

Allesøe, R. L., Lundgaard, A. T., Hernández Medina, R., Aguayo-Orozco, A., Johansen, J., Nissen, J. N., Brorsson, C., Mazzoni, G., Niu, L., Biel, J. H., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01520-x · Cited by 111 · see citing works

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Blaser, M. C., Kraler, S., Lüscher, T. F., & Aikawa, E. (2021) · Circulation Research · doi:10.1161/circresaha.120.317979 · Cited by 109 · see citing works

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Pun, F. W., Liu, B. H. M., Long, X., Leung, H. W., Leung, G. H. D., Mewborne, Q. T., Gao, J., Shneyderman, A., Ozerov, I. V., Wang, J., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.914017 · Cited by 115 · see citing works

IFN signaling and neutrophil degranulation transcriptional signatures are induced during SARS-CoV-2 infection

Rosa, B. A., Ahmed, M., Singh, D. K., Choreño-Parra, J. A., Cole, J., Jiménez-Álvarez, L. A., Rodríguez-Reyna, T. S., Singh, B., Gonzalez, O., Carrion, R., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01829-4 · Cited by 106 · see citing works

Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106 · see citing works

Multiomics of Tissue Extracellular Vesicles Identifies Unique Modulators of Atherosclerosis and Calcific Aortic Valve Stenosis

Blaser, M. C., Buffolo, F., Halu, A., Turner, M. E., Schlotter, F., Higashi, H., Pantano, L., Clift, C. L., Saddic, L. A., Atkins, S. K., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.063402 · Cited by 114 · see citing works