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With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools\/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease\u2013disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease\u2013disease associations are first summarized. Then, existing computational methods for disease\u2013disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic\u2013based, phenotype-based, function-based, representation learning\u2013based and text mining\u2013based methods. Further, we summarize software tools\/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease\u2013disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools\/platforms for disease\u2013disease associations.<\/jats:p>","DOI":"10.1093\/bib\/bbac006","type":"journal-article","created":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T12:07:33Z","timestamp":1642248453000},"source":"Crossref","is-referenced-by-count":30,"title":["Biomedical data, computational methods and tools for evaluating disease\u2013disease associations"],"prefix":"10.1093","volume":"23","author":[{"given":"Ju","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, China"}]},{"given":"Jiashuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China"}]},{"given":"Yichao","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6189-4795","authenticated-orcid":false,"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6017-0701","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada"}]}],"member":"286","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"2022031506314115400_ref1","doi-asserted-by":"crossref","DOI":"10.1596\/978-92-4-151355-5","volume-title":"Tracking Universal Health Coverage: 2017 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analysis of complex disease: integrative methods for multi-omics data","volume":"19","author":"Yan","year":"2017","journal-title":"Brief Bioinform"},{"key":"2022031506314115400_ref50","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1093\/bib\/bbx151","article-title":"Systems bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches","volume":"20","author":"Oulas","year":"2017","journal-title":"Brief Bioinform"},{"key":"2022031506314115400_ref51","doi-asserted-by":"crossref","DOI":"10.1093\/database\/bay011","article-title":"BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology","volume":"2018","author":"Lakiotaki","year":"2018","journal-title":"Database"},{"key":"2022031506314115400_ref52","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1093\/bib\/bbx158","article-title":"Computer-aided biomarker discovery for precision medicine: data resources, models 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ontologies","volume":"85","author":"K\u00f6hler","year":"2009","journal-title":"Am J Hum Genet"},{"key":"2022031506314115400_ref61","doi-asserted-by":"crossref","first-page":"R7","DOI":"10.1186\/gb-2004-6-1-r7","article-title":"The mammalian phenotype ontology as a tool for annotating, analyzing and comparing phenotypic information","volume":"6","author":"Smith","year":"2004","journal-title":"Genome Biol"},{"key":"2022031506314115400_ref62","doi-asserted-by":"crossref","first-page":"D833","DOI":"10.1093\/nar\/gkw943","article-title":"DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants","volume":"45","author":"Pi\u00f1ero","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2022031506314115400_ref63","doi-asserted-by":"crossref","first-page":"D977","DOI":"10.1093\/nar\/gkx1049","article-title":"PedAM: a database for pediatric disease annotation and medicine","volume":"46","author":"Jia","year":"2017","journal-title":"Nucleic 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Biosyst"},{"key":"2022031506314115400_ref143","doi-asserted-by":"crossref","first-page":"D857","DOI":"10.1093\/nar\/gkr930","article-title":"MINT, the molecular interaction database: 2012 update","volume":"40","author":"Licata","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2022031506314115400_ref144","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2022031506314115400_ref145","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1038\/nmeth.2561","article-title":"Mentha: a resource for browsing integrated protein-interaction networks","volume":"10","author":"Calderone","year":"2013","journal-title":"Nat 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tissues reveals novel genes and molecular pathways underlying major depression","volume":"15","author":"Gerring","year":"2019","journal-title":"PLoS Genet"},{"key":"2022031506314115400_ref150","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1093\/bib\/bby067","article-title":"Interpretation of differential gene expression results of RNA-seq data: review and integration","volume":"20","author":"McDermaid","year":"2018","journal-title":"Brief Bioinform"},{"key":"2022031506314115400_ref151","first-page":"575","article-title":"Gene co-expression analysis for functional classification and gene\u2013disease predictions","volume":"19","author":"Dam","year":"2018","journal-title":"Brief Bioinform"},{"key":"2022031506314115400_ref152","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1126\/science.1262110","article-title":"The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in 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interactions","volume":"48","author":"Teng","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2022031506314115400_ref160","doi-asserted-by":"crossref","first-page":"D1233","DOI":"10.1093\/nar\/gkaa755","article-title":"INTEDE: interactome of drug-metabolizing enzymes","volume":"49","author":"Yin","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2022031506314115400_ref161","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2022031506314115400_ref162","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1093\/bib\/bby029","article-title":"Disease prediction by cell-free DNA methylation","volume":"20","author":"Feng","year":"2018","journal-title":"Brief 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to improve the GO-based similarity measure between proteins","author":"Li","year":"2010"},{"key":"2022031506314115400_ref184","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1145\/1099554.1099658","volume-title":"Proceedings of the 14th ACM International Conference on Information and Knowledge Management","author":"Couto","year":"2005"},{"key":"2022031506314115400_ref185","doi-asserted-by":"crossref","first-page":"e0235670","DOI":"10.1371\/journal.pone.0235670","article-title":"UFO: a tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization","volume":"15","author":"Le","year":"2020","journal-title":"Plos One"},{"key":"2022031506314115400_ref186","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1093\/bioinformatics\/btm087","article-title":"A new method to measure the semantic similarity of GO 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lists","volume":"8","author":"Huang","year":"2007","journal-title":"Genome Biol"},{"key":"2022031506314115400_ref193","volume-title":"Visualizing and Distances Using GO","author":"Gentleman","year":"2005"},{"key":"2022031506314115400_ref194","doi-asserted-by":"crossref","first-page":"S4","DOI":"10.1186\/1471-2105-9-S5-S4","article-title":"Metrics for GO based protein semantic similarity: a systematic evaluation","volume":"9","author":"Pesquita","year":"2008","journal-title":"BMC Bioinform"},{"key":"2022031506314115400_ref195","doi-asserted-by":"crossref","first-page":"2005.0026","DOI":"10.1038\/msb4100034","article-title":"Gene function prediction from congruent synthetic lethal interactions in yeast","volume":"1","author":"Ye","year":"2005","journal-title":"Mol Syst Biol"},{"key":"2022031506314115400_ref196","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1038\/s41436-018-0072-y","article-title":"Phrank measures phenotype sets similarity to greatly improve Mendelian 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(BIBM)","author":"Peng","year":"2016"},{"key":"2022031506314115400_ref200","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1186\/s12864-018-4927-z","article-title":"An online tool for measuring and visualizing phenotype similarities using HPO","volume":"19","author":"Peng","year":"2018","journal-title":"BMC Genom"},{"key":"2022031506314115400_ref201","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s12918-019-0697-8","article-title":"Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO","volume":"13","author":"Xue","year":"2019","journal-title":"BMC Syst Biol"},{"key":"2022031506314115400_ref202","doi-asserted-by":"crossref","first-page":"11694","DOI":"10.1073\/pnas.0704820104","article-title":"Probing genetic overlap among complex human phenotypes","volume":"104","author":"Rzhetsky","year":"2007","journal-title":"Proc Natl Acad Sci"},{"key":"2022031506314115400_ref203","doi-asserted-by":"crossref","first-page":"R91","DOI":"10.1186\/gb-2009-10-9-r91","article-title":"Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network","volume":"10","author":"Linghu","year":"2009","journal-title":"Genome Biol"},{"key":"2022031506314115400_ref204","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1038\/msb4100163","article-title":"Human disease classification in the postgenomic era: a complex systems approach to human pathobiology","volume":"3","author":"Loscalzo","year":"2007","journal-title":"Mol Syst Biol"},{"key":"2022031506314115400_ref205","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nrg1327","article-title":"Genomic variants in exons and introns: identifying the splicing spoilers","volume":"5","author":"Pagani","year":"2004","journal-title":"Nat Rev 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One"},{"key":"2022031506314115400_ref209","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1136\/amiajnl-2011-000482","article-title":"Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory","volume":"19","author":"Li","year":"2012","journal-title":"J Am Med Inform Assoc"},{"key":"2022031506314115400_ref210","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ajhg.2018.11.006","article-title":"Human-disease phenotype map derived from PheWAS across 38,682 individuals","volume":"104","author":"Verma","year":"2019","journal-title":"Am J Hum Genet"},{"key":"2022031506314115400_ref211","doi-asserted-by":"crossref","first-page":"i437","DOI":"10.1093\/bioinformatics\/btw439","article-title":"Causality modeling for directed disease network","volume":"32","author":"Bang","year":"2016","journal-title":"Bioinformatics"},{"key":"2022031506314115400_ref212","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1093\/bfgp\/els032","article-title":"Exploring the human diseasome: the human disease network","volume":"11","author":"Goh","year":"2012","journal-title":"Brief Funct Genomics"},{"key":"2022031506314115400_ref213","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1038\/ejhg.2011.30","article-title":"The expanded human disease network combining protein\u2013protein interaction information","volume":"19","author":"Zhang","year":"2011","journal-title":"Eur J Hum Genet"},{"key":"2022031506314115400_ref214","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1186\/1471-2105-15-304","article-title":"Predicting disease associations via biological network analysis","volume":"15","author":"Sun","year":"2014","journal-title":"BMC 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