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Abstract

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target–disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target–disease associations (). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

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2024-08-23
2025-02-17
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Literature Cited

  1. 1.
    DiMasi JA, Wilkinson M. 2020.. The financial benefits of faster development times: integrated formulation development, real-time manufacturing, and clinical testing. . Ther. Innov. Regul. Sci. 54:(6):145360
    [Crossref] [Google Scholar]
  2. 2.
    Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. 2014.. Clinical development success rates for investigational drugs. . Nat. Biotechnol. 32:(1):4051
    [Crossref] [Google Scholar]
  3. 3.
    Deloitte Cent. Health Solut. 2022.. Nurturing growth: measuring the return from pharmaceutical innovation 2021. Rep. , Deloitte Cent. Health Solut., London:. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/Measuring-the-return-of-pharmaceutical-innovation-2021-Deloitte.pdf
    [Google Scholar]
  4. 4.
    Ochoa D, Hercules A, Carmona M, Suveges D, Gonzalez-Uriarte A, et al. 2021.. Open Targets Platform: supporting systematic drug–target identification and prioritisation. . Nucleic Acids Res. 49:(D1):D130210
    [Crossref] [Google Scholar]
  5. 5.
    Ghoussaini M, Mountjoy E, Carmona M, Peat G, Schmidt EM, et al. 2021.. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. . Nucleic Acids Res. 49:(D1):D131120
    [Crossref] [Google Scholar]
  6. 6.
    Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, et al. 2015.. The support of human genetic evidence for approved drug indications. . Nat. Genet. 47:(8):85660
    [Crossref] [Google Scholar]
  7. 7.
    Ochoa D, Karim M, Ghoussaini M, Hulcoop DG, McDonagh EM, Dunham I. 2022.. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. . Nat. Rev. Drug Discov. 21:(8):551
    [Crossref] [Google Scholar]
  8. 8.
    Rusina PV, Falaguera MJ, Romero JMR, McDonagh EM, Dunham I, Ochoa D. 2023.. Genetic support for FDA-approved drugs over the past decade. . Nat. Rev. Drug Discov. 22:(11):864
    [Crossref] [Google Scholar]
  9. 9.
    Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, et al. 2019.. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. . Nucleic Acids Res. 47:(D1):D100512
    [Crossref] [Google Scholar]
  10. 10.
    Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, et al. 2021.. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. . Nat. Genet. 53:(9):129099
    [Crossref] [Google Scholar]
  11. 11.
    Soskic B, Cano-Gamez E, Smyth DJ, Ambridge K, Ke Z, et al. 2022.. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. . Nat. Genet. 54:(6):81726
    [Crossref] [Google Scholar]
  12. 12.
    Schwartzentruber J, Cooper S, Liu JZ, Barrio-Hernandez I, Bello E, et al. 2021.. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. . Nat. Genet. 53:(3):392402
    [Crossref] [Google Scholar]
  13. 13.
    Barrio-Hernandez I, Schwartzentruber J, Shrivastava A, Del-Toro N, Gonzalez A, et al. 2023.. Network expansion of genetic associations defines a pleiotropy map of human cell biology. . Nat. Genet. 55:(3):38998
    [Crossref] [Google Scholar]
  14. 14.
    Mountjoy E, Schmidt EM, Carmona M, Schwartzentruber J, Peat G, et al. 2021.. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. . Nat. Genet. 53:(11):152733
    [Crossref] [Google Scholar]
  15. 15.
    Krzak M, Alegbe T, Taylor DL, Ghouraba M, Strickland M, et al. 2023.. Single-cell RNA sequencing reveals dysregulated cellular programmes in the inflamed epithelium of Crohn's disease patients. . medRxiv 2023.09.06.23295056. https://doi.org/10.1101/2023.09.06.23295056
  16. 16.
    Huang H, Fang M, Jostins L, Umićević Mirkov M, Boucher G, et al. 2017.. Fine-mapping inflammatory bowel disease loci to single-variant resolution. . Nature 547:(7662):17378
    [Crossref] [Google Scholar]
  17. 17.
    Cooper SE, Schwartzentruber J, Bello E, Coomber EL, Bassett AR. 2020.. Screening for functional transcriptional and splicing regulatory variants with GenIE. . Nucleic Acids Res. 48:(22):e131
    [Crossref] [Google Scholar]
  18. 18.
    Trynka G, Sandor C, Han B, Xu H, Stranger BE, et al. 2013.. Chromatin marks identify critical cell types for fine mapping complex trait variants. . Nat. Genet. 45:(2):12430
    [Crossref] [Google Scholar]
  19. 19.
    Soskic B, Cano-Gamez E, Smyth DJ, Rowan WC, Nakic N, et al. 2019.. Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases. . Nat. Genet. 51:(10):148693
    [Crossref] [Google Scholar]
  20. 20.
    Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, et al. 2019.. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. . Nature 568:(7753):51116
    [Crossref] [Google Scholar]
  21. 21.
    Chan EM, Shibue T, McFarland JM, Gaeta B, Ghandi M, et al. 2019.. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. . Nature 568:(7753):55156
    [Crossref] [Google Scholar]
  22. 22.
    Pacini C, Dempster JM, Boyle I, Gonçalves E, Najgebauer H, et al. 2021.. Integrated cross-study datasets of genetic dependencies in cancer. . Nat. Commun. 12:(1):1661
    [Crossref] [Google Scholar]
  23. 23.
    Dwane L, Behan FM, Gonçalves E, Lightfoot H, Yang W, et al. 2021.. Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets. . Nucleic Acids Res. 49:(D1):D136572
    [Crossref] [Google Scholar]
  24. 24.
    King EA, Davis JW, Degner JF. 2019.. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. . PLOS Genet. 15:(12):e1008489
    [Crossref] [Google Scholar]
  25. 25.
    Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, et al. 2018.. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. . Nat. Genet. 50:(9):133541
    [Crossref] [Google Scholar]
  26. 26.
    Malone J, Holloway E, Adamusiak T, Kapushesky M, Zheng J, et al. 2010.. Modeling sample variables with an Experimental Factor Ontology. . Bioinformatics 26:(8):111218
    [Crossref] [Google Scholar]
  27. 27.
    Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, et al. 2023.. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. . Nucleic Acids Res. 51:(D1):D97785
    [Crossref] [Google Scholar]
  28. 28.
    GTEx Consort. 2020.. The GTEx Consortium atlas of genetic regulatory effects across human tissues. . Science 369:(6509):131830
    [Crossref] [Google Scholar]
  29. 29.
    Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, et al. 2018.. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. . bioRxiv 447367. https://doi.org/10.1101/447367
  30. 30.
    Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, et al. 2018.. Genomic atlas of the human plasma proteome. . Nature 558:(7708):7379
    [Crossref] [Google Scholar]
  31. 31.
    Folkersen L, Fauman E, Sabater-Lleal M, Strawbridge RJ, Frånberg M, et al. 2017.. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. . PLOS Genet. 13:(4):e1006706
    [Crossref] [Google Scholar]
  32. 32.
    Hillary RF, McCartney DL, Harris SE, Stevenson AJ, Seeboth A, et al. 2019.. Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936. . Nat. Commun. 10:(1):3160
    [Crossref] [Google Scholar]
  33. 33.
    Ahola-Olli AV, Würtz P, Havulinna AS, Aalto K, Pitkänen N, et al. 2017.. Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. . Am. J. Hum. Genet. 100:(1):4050
    [Crossref] [Google Scholar]
  34. 34.
    Pietzner M, Stewart ID, Raffler J, Khaw K-T, Michelotti GA, et al. 2021.. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. . Nat. Med. 27:(3):47179
    [Crossref] [Google Scholar]
  35. 35.
    Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, et al. 2017.. Connecting genetic risk to disease end points through the human blood plasma proteome. . Nat. Commun. 8:(1):14357
    [Crossref] [Google Scholar]
  36. 36.
    Folkersen L, Gustafsson S, Wang Q, Hansen DH, Hedman ÅK, et al. 2020.. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. . Nat. Metab. 2:(10):113548
    [Crossref] [Google Scholar]
  37. 37.
    McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, et al. 2016.. The Ensembl Variant Effect Predictor. . Genome Biol. 17:(1):122
    [Crossref] [Google Scholar]
  38. 38.
    Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, et al. 2016.. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. . Cell 167:(5):136984.e19
    [Crossref] [Google Scholar]
  39. 39.
    Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, et al. 2014.. An atlas of active enhancers across human cell types and tissues. . Nature 507:(7493):45561
    [Crossref] [Google Scholar]
  40. 40.
    Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. 2012.. The accessible chromatin landscape of the human genome. . Nature 489:(7414):7582
    [Crossref] [Google Scholar]
  41. 41.
    Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, et al. 2015.. ChEMBL web services: streamlining access to drug discovery data and utilities. . Nucleic Acids Res. 43:(W1):W61220
    [Crossref] [Google Scholar]
  42. 42.
    Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, et al. 2019.. ChEMBL: towards direct deposition of bioassay data. . Nucleic Acids Res. 47:(D1):D93040
    [Crossref] [Google Scholar]
  43. 43.
    Stacey D, Fauman EB, Ziemek D, Sun BB, Harshfield EL, et al. 2019.. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. . Nucleic Acids Res. 47:(1):e3
    [Crossref] [Google Scholar]
  44. 44.
    van der Wijst M, de Vries DH, Groot HE, Trynka G, Hon CC, et al. 2020.. The single-cell eQTLGen consortium. . eLife 9::e52155
    [Crossref] [Google Scholar]
  45. 45.
    Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, et al. 2023.. Ensembl 2023. . Nucleic Acids Res. 51:(D1):D93341
    [Crossref] [Google Scholar]
  46. 46.
    Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M, et al. 2017.. Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource. . Am. J. Hum. Genet. 100:(6):895906
    [Crossref] [Google Scholar]
  47. 47.
    Martin AR, Williams E, Foulger RE, Leigh S, Daugherty LC, et al. 2019.. PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. . Nat. Genet. 51:(11):156065
    [Crossref] [Google Scholar]
  48. 48.
    Thormann A, Halachev M, McLaren W, Moore DJ, Svinti V, et al. 2019.. Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP. . Nat. Commun. 10:(1):2373
    [Crossref] [Google Scholar]
  49. 49.
    Maiella S, Rath A, Angin C, Mousson F, Kremp O. 2013.. Orphanet et son réseau: où trouver une information validée sur les maladies rares [Orphanet and its consortium: where to find expert-validated information on rare diseases]. . Rev. Neurol. 169:(Suppl. 1):S38
    [Crossref] [Google Scholar]
  50. 50.
    DiStefano MT, Goehringer S, Babb L, Alkuraya FS, Amberger J, et al. 2022.. The Gene Curation Coalition: a global effort to harmonize gene–disease evidence resources. . Genet. Med. 24:(8):173242
    [Crossref] [Google Scholar]
  51. 51.
    Wang Q, Dhindsa RS, Carss K, Harper AR, Nag A, et al. 2021.. Rare variant contribution to human disease in 281,104 UK Biobank exomes. . Nature 597:(7877):52732
    [Crossref] [Google Scholar]
  52. 52.
    Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, et al. 2021.. Exome sequencing and analysis of 454,787 UK Biobank participants. . Nature 599:(7886):62834
    [Crossref] [Google Scholar]
  53. 53.
    Karczewski KJ, Solomonson M, Chao KR, Goodrich JK, Tiao G, et al. 2022.. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. . Cell Genom. 2:(9):100168
    [Crossref] [Google Scholar]
  54. 54.
    Zhou X, Feliciano P, Shu C, Wang T, Astrovskaya I, et al. 2022.. Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes. . Nat. Genet. 54:(9):130519
    [Crossref] [Google Scholar]
  55. 55.
    Singh T, Poterba T, Curtis D, Akil H, Al Eissa M, et al. 2022.. Rare coding variants in ten genes confer substantial risk for schizophrenia. . Nature 604:(7906):50916
    [Crossref] [Google Scholar]
  56. 56.
    Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, et al. 2020.. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. . Cell 180:(3):56884.e23
    [Crossref] [Google Scholar]
  57. 57.
    Epi25 Collab. 2019.. Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals. . Am. J. Hum. Genet. 105:(2):26782
    [Crossref] [Google Scholar]
  58. 58.
    Bomba L, Walter K, Guo Q, Surendran P, Kundu K, et al. 2022.. Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites. . Am. J. Hum. Genet. 109:(6):103854
    [Crossref] [Google Scholar]
  59. 59.
    Akbari P, Sosina OA, Bovijn J, Landheer K, Nielsen JB, et al. 2022.. Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes. . Nat. Commun. 13:(1):4844
    [Crossref] [Google Scholar]
  60. 60.
    Makarious MB, Lake J, Pitz V, Ye Fu A, Guidubaldi JL, et al. 2023.. Large-scale rare variant burden testing in Parkinson's disease. . Brain 146:(11):462232
    [Crossref] [Google Scholar]
  61. 61.
    Riveros-Mckay F, Oliver-Williams C, Karthikeyan S, Walter K, Kundu K, et al. 2020.. The influence of rare variants in circulating metabolic biomarkers. . PLOS Genet. 16:(3):e1008605
    [Crossref] [Google Scholar]
  62. 62.
    Futreal PA, Coin L, Marshall M, Down T, Hubbard T, et al. 2004.. A census of human cancer genes. . Nat. Rev. Cancer 4:(3):17783
    [Crossref] [Google Scholar]
  63. 63.
    Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, et al. 2019.. COSMIC: the Catalogue Of Somatic Mutations In Cancer. . Nucleic Acids Res. 47:(D1):D94147
    [Crossref] [Google Scholar]
  64. 64.
    Martínez-Jiménez F, Muiños F, Sentís I, Deu-Pons J, Reyes-Salazar I, et al. 2020.. A compendium of mutational cancer driver genes. . Nat. Rev. Cancer 20:(10):55572
    [Crossref] [Google Scholar]
  65. 65.
    Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, et al. 2018.. ClinVar: improving access to variant interpretations and supporting evidence. . Nucleic Acids Res. 46:(D1):D106267
    [Crossref] [Google Scholar]
  66. 66.
    Ghoussaini M, Nelson MR, Dunham I. 2023.. Future prospects for human genetics and genomics in drug discovery. . Curr. Opin. Struct. Biol. 80::102568
    [Crossref] [Google Scholar]
  67. 67.
    Schneider M, Radoux CJ, Hercules A, Ochoa D, Dunham I, et al. 2021.. The PROTACtable genome. . Nat. Rev. Drug Discov. 20:(10):78997
    [Crossref] [Google Scholar]
  68. 68.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021.. Highly accurate protein structure prediction with AlphaFold. . Nature 596:(7873):58389
    [Crossref] [Google Scholar]
  69. 69.
    Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, et al. 2021.. Highly accurate protein structure prediction for the human proteome. . Nature 596:(7873):59096
    [Crossref] [Google Scholar]
  70. 70.
    Roberts TC, Wood MJA, Davies KE. 2023.. Therapeutic approaches for Duchenne muscular dystrophy. . Nat. Rev. Drug Discov. 22:(11):91734
    [Crossref] [Google Scholar]
  71. 71.
    Müller S, Ackloo S, Al Chawaf A, Al-Lazikani B, Antolin A, et al. 2022.. Target 2035 - update on the quest for a probe for every protein. . RSC Med. Chem. 13:(1):1321
    [Crossref] [Google Scholar]
  72. 72.
    Bowes J, Brown AJ, Hamon J, Jarolimek W, Sridhar A, et al. 2012.. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. . Nat. Rev. Drug Discov. 11:(12):90922
    [Crossref] [Google Scholar]
  73. 73.
    Force T, Kolaja KL. 2011.. Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes. . Nat. Rev. Drug Discov. 10:(2):11126
    [Crossref] [Google Scholar]
  74. 74.
    Richard AM, Judson RS, Houck KA, Grulke CM, Volarath P, et al. 2016.. ToxCast chemical landscape: paving the road to 21st century toxicology. . Chem. Res. Toxicol. 29:(8):122551
    [Crossref] [Google Scholar]
  75. 75.
    Lamore SD, Ahlberg E, Boyer S, Lamb ML, Hortigon-Vinagre MP, et al. 2017.. Deconvoluting kinase inhibitor induced cardiotoxicity. . Toxicol. Sci. 158:(1):21326
    [Crossref] [Google Scholar]
  76. 76.
    Lynch JJ, Van Vleet TR, Mittelstadt SW, Blomme EAG. 2017.. Potential functional and pathological side effects related to off-target pharmacological activity. . J. Pharmacol. Toxicol. Methods 87::10826
    [Crossref] [Google Scholar]
  77. 77.
    Urban L, Whitebread S, Hamon J, Mikhailov D, Azzaoui K. 2012.. Screening for safety-relevant off-target activities. . In Polypharmacology in Drug Discovery, ed. J-U Peters , pp. 1546. Hoboken, NJ:: Wiley
    [Google Scholar]
  78. 78.
    Carss KJ, Deaton AM, Del Rio-Espinola A, Diogo D, Fielden M, et al. 2023.. Using human genetics to improve safety assessment of therapeutics. . Nat. Rev. Drug Discov. 22:(2):14562
    [Crossref] [Google Scholar]
  79. 79.
    Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, et al. 2022.. A genome-wide mutational constraint map quantified from variation in 76,156 human genomes. . bioRxiv 2022.03.20.485034. https://doi.org/10.1101/2022.03.20.485034
  80. 80.
    Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, et al. 2020.. The mutational constraint spectrum quantified from variation in 141,456 humans. . Nature 581::43443
    [Crossref] [Google Scholar]
  81. 81.
    Papatheodorou I, Moreno P, Manning J, Fuentes AM-P, George N, et al. 2020.. Expression Atlas update: from tissues to single cells. . Nucleic Acids Res. 48:(D1):D7783
    [Google Scholar]
  82. 82.
    Blake JA, Baldarelli R, Kadin JA, Richardson JE, Smith CL, et al. 2021.. Mouse Genome Database (MGD): knowledgebase for mouse-human comparative biology. . Nucleic Acids Res. 49:(D1):D98187
    [Crossref] [Google Scholar]
  83. 83.
    Maciejewski M, Lounkine E, Whitebread S, Farmer P, DuMouchel W, et al. 2017.. Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets. . eLife 6::e25818
    [Crossref] [Google Scholar]
  84. 84.
    Ochoa D, Hercules A, Carmona M, Suveges D, Baker J, et al. 2023.. The next-generation Open Targets Platform: reimagined, redesigned, rebuilt. . Nucleic Acids Res. 51:(D1):D135359
    [Crossref] [Google Scholar]
  85. 85.
    Razuvayevskaya O, Lopez I, Dunham I, Ochoa D. 2023.. Why clinical trials stop: the role of genetics. . medRxiv 2023.02.07.23285407. https://doi.org/10.1101/2023.02.07.23285407
  86. 86.
    Han Y, Klinger K, Rajpal DK, Zhu C, Teeple E. 2022.. Empowering the discovery of novel target-disease associations via machine learning approaches in the Open Targets platform. . BMC Bioinform. 23:(1):232
    [Crossref] [Google Scholar]
  87. 87.
    Gogleva A, Polychronopoulos D, Pfeifer M, Poroshin V, Ughetto M, et al. 2022.. Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer. . Nat. Commun. 13:(1):1667
    [Crossref] [Google Scholar]
  88. 88.
    Ye C, Swiers R, Bonner S, Barrett I. 2022.. A knowledge graph-enhanced tensor factorisation model for discovering drug targets. . IEEE/ACM Trans. Comput. Biol. Bioinform. 19:(6):307080
    [Crossref] [Google Scholar]
  89. 89.
    Fernández-Torras A, Duran-Frigola M, Bertoni M, Locatelli M, Aloy P. 2022.. Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque. . Nat. Commun. 13:(1):5304
    [Crossref] [Google Scholar]
  90. 90.
    Geleta D, Nikolov A, Edwards G, Gogleva A, Jackson R, et al. 2021.. Biological Insights Knowledge Graph: an integrated knowledge graph to support drug development. . bioRxiv 2021.10.28.466262. https://doi.org/10.1101/2021.10.28.466262
  91. 91.
    Paliwal S, de Giorgio A, Neil D, Michel J-B, Lacoste AM. 2020.. Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs. . Sci. Rep. 10:(1):18250
    [Crossref] [Google Scholar]
  92. 92.
    Failli M, Paananen J, Fortino V. 2019.. Prioritizing target-disease associations with novel safety and efficacy scoring methods. . Sci. Rep. 9:(1):9852
    [Crossref] [Google Scholar]
  93. 93.
    Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, et al. 2022.. StarGazer: a hybrid intelligence platform for drug target prioritization and digital drug repositioning using Streamlit. . Front. Genet. 13::868015
    [Crossref] [Google Scholar]
  94. 94.
    Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, et al. 2022.. The European Variation Archive: a FAIR resource of genomic variation for all species. . Nucleic Acids Res. 50:(D1):D121620
    [Crossref] [Google Scholar]
  95. 95.
    Moreno P, Fexova S, George N, Manning JR, Miao Z, et al. 2022.. Expression Atlas update: gene and protein expression in multiple species. . Nucleic Acids Res. 50:(D1):D12940
    [Crossref] [Google Scholar]
  96. 96.
    Varusai TM, Jupe S, Sevilla C, Matthews L, Gillespie M, et al. 2021.. Using Reactome to build an autophagy mechanism knowledgebase. . Autophagy 17:(6):154354
    [Crossref] [Google Scholar]
  97. 97.
    Shameer K, Badgeley MA, Miotto R, Glicksberg BS, Morgan JW, Dudley JT. 2017.. Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. . Brief. Bioinform. 18:(1):10524
    [Crossref] [Google Scholar]
  98. 98.
    Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, et al. 2021.. The role of machine learning in clinical research: transforming the future of evidence generation. . Trials 22:(1):537
    [Crossref] [Google Scholar]
  99. 99.
    Int. Mult. Scler. Genet. Consort., MultipleMS Consort. 2023.. Locus for severity implicates CNS resilience in progression of multiple sclerosis. . Nature 619:(7969):32331
    [Crossref] [Google Scholar]
  100. 100.
    Dendrou CA, Cortes A, Shipman L, Evans HG, Attfield KE, et al. 2016.. Resolving TYK2 locus genotype-to-phenotype differences in autoimmunity. . Sci. Transl. Med. 8:(363):363ra149
    [Crossref] [Google Scholar]
  101. 101.
    McGregor TL, Hunt KA, Yee E, Mason D, Nioi P, et al. 2020.. Characterising a healthy adult with a rare HAO1 knockout to support a therapeutic strategy for primary hyperoxaluria. . eLife 9::e54363
    [Crossref] [Google Scholar]
  102. 102.
    Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J, et al. 2021.. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. . Nat. Genet. 53:(3):30412
    [Crossref] [Google Scholar]
  103. 103.
    Tambets R, Kolde A, Kolberg P, Love MI, Alasoo K. 2023.. Extensive co-regulation of neighbouring genes complicates the use of eQTLs in target gene prioritization. . bioRxiv 2023.09.29.560109. https://doi.org/10.1101/2023.09.29.560109
  104. 104.
    Ndungu A, Payne A, Torres JM, van de Bunt M, McCarthy MI. 2020.. A multi-tissue transcriptome analysis of human metabolites guides interpretability of associations based on multi-SNP models for gene expression. . Am. J. Hum. Genet. 106:(2):188201
    [Crossref] [Google Scholar]
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