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It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.<\/jats:p>","DOI":"10.1093\/bib\/bbab314","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:08:58Z","timestamp":1628680138000},"source":"Crossref","is-referenced-by-count":6,"title":["A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery"],"prefix":"10.1093","volume":"22","author":[{"given":"Teemu J","family":"Rintala","sequence":"first","affiliation":[{"name":"Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland"}]},{"given":"Antonio","family":"Federico","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Technology Tampere University, Kalevantie, 4 33100 Tampere, Finland"},{"name":"BioMediTech Institute Tampere University, Kalevantie 4, 33100 Tampere, Finland"}]},{"given":"Leena","family":"Latonen","sequence":"additional","affiliation":[{"name":"Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland"}]},{"given":"Dario","family":"Greco","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Technology Tampere University, Kalevantie, 4 33100 Tampere, Finland"},{"name":"BioMediTech Institute Tampere University, Kalevantie 4, 33100 Tampere, Finland"},{"name":"Institute of Biotechnology University of Helsinki, Viikinkaari 5d, 00014 Helsinki, Finland"}]},{"given":"Vittorio","family":"Fortino","sequence":"additional","affiliation":[{"name":"Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland"}]}],"member":"286","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"2021110815083063500_ref1","article-title":"TCGA batch effects viewer","author":"Akbani"},{"issue":"6","key":"2021110815083063500_ref2","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. 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