Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders

The GeneCards suite: from gene data mining to disease genome sequence analyses

Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Stein, T. I., Nudel, R., Lieder, I., Mazor, Y., et al. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.5 · Cited by 5,550

The human disease network

Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242

A circadian gene expression atlas in mammals: Implications for biology and medicine

Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E., & Hogenesch, J. B. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408886111 · Cited by 2,304

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Taliun, D., Harris, D. N., Kessler, M. D., Carlson, J., Szpiech, Z. A., Torres, R., Taliun, S. A. G., Corvelo, A., Gogarten, S. M., Kang, H. M., et al. (2021) · Nature · doi:10.1038/s41586-021-03205-y · Cited by 2,213

A census of human RNA-binding proteins

Gerstberger, S., Hafner, M., & Tuschl, T. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3813 · Cited by 2,032

Uncovering disease-disease relationships through the incomplete interactome

Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015) · Science · doi:10.1126/science.1257601 · Cited by 1,546

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins

Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., & Ma’ayan, A. (2016) · Database · doi:10.1093/database/baw100 · Cited by 1,515

Global reconstruction of the human metabolic network based on genomic and bibliomic data

Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610772104 · Cited by 1,374

High-throughput discovery of novel developmental phenotypes

The International Mouse Phenotyping Consortium, Dickinson, M. E., Flenniken, A. M., Ji, X., Teboul, L., Wong, M. D., White, J. K., Meehan, T. F., Weninger, W. J., Westerberg, H., et al. (2016) · Nature · doi:10.1038/nature19356 · Cited by 1,196

Rare and common variants: twenty arguments

Gibson, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3118 · Cited by 1,278

Somatic coding mutations in human induced pluripotent stem cells

Gore, A., Li, Z., Fung, H.-L., Young, J. E., Agarwal, S., Antosiewicz-Bourget, J., Canto, I., Giorgetti, A., Israel, M. A., Kiskinis, E., et al. (2011) · Nature · doi:10.1038/nature09805 · Cited by 1,273

Database resources of the National Center for Biotechnology Information

Wheeler, D. L., Barrett, T., Benson, D. A., Bryant, S. H., Canese, K., Chetvernin, V., Church, D. M., DiCuccio, M., Edgar, R., Federhen, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1000 · Cited by 1,007

The human phenotype ontology: a tool for annotating and analyzing human hereditary disease

Robinson, P. N., Köhler, S., Bauer, S., Seelow, D., Horn, D., & Mundlos, S. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.017 · Cited by 1,034

An integrative approach to ortholog prediction for disease-focused and other functional studies

Hu, Y., Flockhart, I., Vinayagam, A., Bergwitz, C., Berger, B., Perrimon, N., & Mohr, S. E. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-357 · Cited by 848

Understanding multicellular function and disease with human tissue-specific networks

Greene, C. S., Krishnan, A., Wong, A. K., Ricciotti, E., Zelaya, R. A., Himmelstein, D. S., Zhang, R., Hartmann, B. M., Zaslavsky, E., Sealfon, S. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3259 · Cited by 832

Protein networks in disease

Ideker, T., & Sharan, R. (2008) · Genome Research · doi:10.1101/gr.071852.107 · Cited by 817

McKusick's Online Mendelian Inheritance in Man (OMIM(R))

Amberger, J., Bocchini, C. A., Scott, A. F., & Hamosh, A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn665 · Cited by 717

Minimum information requested in the annotation of biochemical models (MIRIAM)

Novère, N. L., Finney, A., Hucka, M., Bhalla, U. S., Campagne, F., Collado-Vides, J., Crampin, E. J., Halstead, M., Klipp, E., Mendes, P., et al. (2005) · Nature Biotechnology · doi:10.1038/nbt1156 · Cited by 622

Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies

Köhler, S., Schulz, M. H., Krawitz, P., Bauer, S., Dölken, S., Ott, C. E., Mundlos, C., Horn, D., Mundlos, S., & Robinson, P. N. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.09.003 · Cited by 590

Better prediction of functional effects for sequence variants

Hecht, M., Bromberg, Y., & Rost, B. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s8-s1 · Cited by 569

TreeFam: a curated database of phylogenetic trees of animal gene families

Li, H. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj118 · Cited by 523

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

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

Franke, L., Bakel, H. V., Fokkens, L., de Jong, E. D., Egmont-Petersen, M., & Wijmenga, C. (2006) · The American Journal of Human Genetics · doi:10.1086/504300 · Cited by 476

The Edinburgh human metabolic network reconstruction and its functional analysis

Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., & Goryanin, I. (2007) · Molecular Systems Biology · doi:10.1038/msb4100177 · Cited by 435

Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia

Genovese, G., Fromer, M., Stahl, E. A., Ruderfer, D. M., Chambert, K., Landén, M., Moran, J. L., Purcell, S. M., Sklar, P., Sullivan, P. F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4402 · Cited by 444

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

Jarada, T. N., Rokne, J. G., & Alhajj, R. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00450-7 · Cited by 384

An Expanded Oct4 Interaction Network: Implications for Stem Cell Biology, Development, and Disease

Pardo, M., Lang, B., Yu, L., Prosser, H., Bradley, A., Babu, M. M., & Choudhary, J. (2010) · Cell Stem Cell · doi:10.1016/j.stem.2010.03.004 · Cited by 379

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts

Undiagnosed Diseases Network, Frésard, L., Care4Rare Canada Consortium, Smail, C., Ferraro, N. M., Teran, N. A., Li, X., Smith, K. S., Bonner, D., Kernohan, K. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0457-8 · Cited by 321

A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes

Lage, K., Hansen, N. T., Karlberg, E. O., Eklund, A. C., Roque, F. S., Donahoe, P. K., Szallasi, Z., Jensen, T. S., & Brunak, S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810772105 · Cited by 292

A critical assessment of Mus musculusgene function prediction using integrated genomic evidence

Peña-Castillo, L., Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. K., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-s1-s2 · Cited by 248

A Nondegenerate Code of Deleterious Variants in Mendelian Loci Contributes to Complex Disease Risk

Blair, D. R., Lyttle, C. S., Mortensen, J. M., Bearden, C. F., Jensen, A. B., Khiabanian, H., Melamed, R., Rabadan, R., Bernstam, E. V., Brunak, S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.08.030 · Cited by 215

Genetic Screen for Cell Fitness in High or Low Oxygen Highlights Mitochondrial and Lipid Metabolism

Jain, I. H., Calvo, S. E., Markhard, A. L., Skinner, O. S., To, T.-L., Ast, T., & Mootha, V. K. (2020) · Cell · doi:10.1016/j.cell.2020.03.029 · Cited by 224

Prediction and Validation of Disease Genes Using HeteSim Scores

Zeng, X., Liao, Y., Liu, Y., & Zou, Q. (2017) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2520947 · Cited by 208

A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat

Xu, R., Wang, Q., & Li, L. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s4 · Cited by 193

Modeling 3D Facial Shape from DNA

Claes, P., Liberton, D. K., Daniels, K., Rosana, K. M., Quillen, E. E., Pearson, L. N., McEvoy, B., Bauchet, M., Zaidi, A. A., Yao, W., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004224 · Cited by 202

TFCat: the curated catalog of mouse and human transcription factors

Fulton, D. L., Sundararajan, S., Badis, G., Hughes, T. R., Wasserman, W. W., Roach, J. C., & Sladek, R. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r29 · Cited by 189

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180

Towards understanding brain-gut-microbiome connections in Alzheimer’s disease

Xu, R., & Wang, Q. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0307-y · Cited by 175

Mapping autosomal recessive intellectual disability: combined microarray and exome sequencing identifies 26 novel candidate genes in 192 consanguineous families

Harripaul, R., Vasli, N., Mikhailov, A., Rafiq, M. A., Mittal, K., Windpassinger, C., Sheikh, T. I., Noor, A., Mahmood, H., Downey, S., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.60 · Cited by 175

Precise modulation of transcription factor levels identifies features underlying dosage sensitivity

Naqvi, S., Kim, S., Hoskens, H., Matthews, H. S., Spritz, R. A., Klein, O. D., Hallgrímsson, B., Swigut, T., Claes, P., Pritchard, J. K., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01366-2 · Cited by 132

Comparative Genomics Search for Losses of Long-Established Genes on the Human Lineage

Zhu, J., Sanborn, J. Z., Diekhans, M., Lowe, C. B., Pringle, T. H., & Haussler, D. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030247 · Cited by 129

GeneFriends: a human RNA-seq-based gene and transcript co-expression database

van Dam, S., Craig, T., & de Magalhães, J. P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1042 · Cited by 115

Metabolic dysregulation in monogenic disorders and cancer — finding method in madness

Erez, A., & DeBerardinis, R. J. (2015) · Nature Reviews Cancer · doi:10.1038/nrc3949 · Cited by 105

Reconstructing Denisovan anatomy using DNA methylation maps

Gokhman, D., Mishol, N., de Manuel, M., de Juan, D., Shuqrun, J., Meshorer, E., Marques-Bonet, T., Rak, Y., & Carmel, L. (2019) · Cell · doi:10.1016/j.cell.2019.08.035 · Cited by 87

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

McInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E. H., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.003 · Cited by 86

Systematic tracking of dysregulated modules identifies novel genes in cancer

Srihari, S., & Ragan, M. A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt191 · Cited by 77

Defective splicing of Megf7/Lrp4, a regulator of distal limb development, in autosomal recessive mulefoot disease

Johnson, E. B., Steffen, D. J., Lynch, K. W., & Herz, J. (2006) · Genomics · doi:10.1016/j.ygeno.2006.08.005 · Cited by 62

High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need

Hughes, R. E., Elliott, R. J. R., Dawson, J. C., & Carragher, N. O. (2021) · Cell Chemical Biology · doi:10.1016/j.chembiol.2021.02.015 · Cited by 62

Human metabolic network reconstruction and its impact on drug discovery and development

Ma, H., & Goryanin, I. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.02.002 · Cited by 59

Biomedical data, computational methods and tools for evaluating disease–disease associations

Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49

Discover the network mechanisms underlying the connections between aging and age-related diseases

Yang, J., Huang, T., Song, W.-M., Petralia, F., Mobbs, C. V., Zhang, B., Zhao, Y., Schadt, E. E., Zhu, J., & Tu, Z. (2016) · Scientific Reports · doi:10.1038/srep32566 · Cited by 46

Integrative modeling reveals the molecular architecture of the intraflagellar transport A (IFT-A) complex

McCafferty, C. L., Papoulas, O., Jordan, M. A., Hoogerbrugge, G., Nichols, C., Pigino, G., Taylor, D. W., Wallingford, J. B., & Marcotte, E. M. (2022) · eLife · doi:10.7554/elife.81977 · Cited by 40

Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases

Alsentzer, E., Li, M. M., Kobren, S. N., Noori, A., Undiagnosed Diseases Network, Kohane, I. S., & Zitnik, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01749-1 · Cited by 54

Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Xiao, Y., Guan, J., Ping, Y., Xu, C., Huang, T., Zhao, H., Fan, H., Li, Y., Lv, Y., Zhao, T., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks538 · Cited by 33

Assessment of the key regulatory genes and their interologs for Turner syndrome employing network approach

Farooqui, A., Tazyeen, S., Ahmed, M. M., Alam, A., Ali, S., Malik, M. Z., Ali, S., & Ishrat, R. (2018) · Scientific Reports · doi:10.1038/s41598-018-28375-0 · Cited by 30

Diagnostic utility of DNA methylation analysis in genetically unsolved pediatric epilepsies and CHD2 episignature refinement

LaFlamme, C. W., Rastin, C., Sengupta, S., Pennington, H. E., Russ-Hall, S. J., Schneider, A. L., Bonkowski, E. S., Almanza Fuerte, E. P., Allan, T. J., Zalusky, M. P.-G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50159-6 · Cited by 31

A genetic variant in miRNA binding site of glutamate receptor 4, metabotropic (GRM4) is associated with increased risk of major depressive disorder

Dadkhah, T., Rahimi-Aliabadi, S., Jamshidi, J., Ghaedi, H., Taghavi, S., Shokraeian, P., Akhavan-Niaki, H., Tafakhori, A., Ohadi, M., & Darvish, H. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.10.008 · Cited by 26

Genome-wide prediction of disease variants with a deep protein language model

Brandes, N., Goldman, G., Wang, C. H., Ye, C. J., & Ntranos, V. (2022) · doi:10.1101/2022.08.25.505311 · Cited by 24

Genetics of female and male reproductive traits and their relationship with health, longevity and consequences for offspring

Benonisdottir, S., Straub, V. J., Kong, A., & Mills, M. C. (2024) · Nature Aging · doi:10.1038/s43587-024-00733-w · Cited by 23

Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients

Yang, Z., Shikany, A., Ni, Y., Zhang, G., Weaver, K. N., & Chen, J. (2022) · Genetics in Medicine · doi:10.1016/j.gim.2022.08.002 · Cited by 20

Effect of Poria cocos Terpenes: Verifying Modes of Action Using Molecular Docking, Drug-Induced Transcriptomes, and Diffusion Network Analyses

Park, M., Yi, J.-M., Kim, N. S., Lee, S.-Y., & Lee, H. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25094636 · Cited by 11

Interpreting noncoding genetic variation in complex traits and human disease

Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6

Integrating Chain‐of‐Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis From Clinical Notes

Wang, Z., Wu, D., Nguyen, Q., & Wang, K. (2026) · Medicine Bulletin · doi:10.1002/mdb2.70033 · Cited by 6

Enhancing variant of uncertain significance (VUS) interpretation in neurogenetics: collaborative experiences from a tertiary care centre

Horowitz, K., Fotopoulos, N. H., Mistry, A. J., Simo, J., Medeiros, M., Bucco, I. D., Ginsberg, M., Dwosh, E., La Piana, R., Rouleau, G. A., et al. (2025) · Journal of Medical Genetics · doi:10.1136/jmg-2024-110122 · Cited by 5

A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Pozzati, G., Zhou, J., Hazan, H., Klement, G. L., Siegelmann, H. T., Tuszynski, J. A., & Rietman, E. A. (2024) · Onco · doi:10.3390/onco4030013 · Cited by 3

The impact of identity-by-descent on fitness and disease in natural and domesticated Canid populations

Mooney, J. A., Yohannes, A., & Lohmueller, K. E. (2020) · doi:10.1101/2020.11.16.385443 · Cited by 1

Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?

Yuan, G. C., Zhang, X., Kim, S. E., & Rajpurkar, P. (2026) · Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026) · doi:10.18653/v1/2026.healing-1.1 · Cited by 4

The landscape of gene loss and missense variation across the mammalian tree informs on gene essentiality

Liao, C., Ye, R., Ivankovic, F., Fu, J. M., Walters, R., Lowther, C., Valkanas, E., Churchhouse, C., Samocha, K. E., Lindblad-Toh, K., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02783-9