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While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org\/clustermine.php<\/jats:p>","DOI":"10.1142\/s0219720020400090","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T04:46:20Z","timestamp":1595479580000},"page":"2040009","source":"Crossref","is-referenced-by-count":3,"title":["ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets"],"prefix":"10.1142","volume":"18","author":[{"given":"Hong-Dong","family":"Li","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P.\u00a0R.\u00a0China"}]},{"given":"Yunpei","family":"Xu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P.\u00a0R.\u00a0China"}]},{"given":"Xiaoshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P.\u00a0R.\u00a0China"},{"name":"School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, P.\u00a0R.\u00a0China"}]},{"given":"Quan","family":"Liu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P.\u00a0R.\u00a0China"}]},{"given":"Gilbert S.","family":"Omenn","sequence":"additional","affiliation":[{"name":"Departments of Computational Medicine and Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan, Ann Arbor, MI 48109-2218, USA"}]},{"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on 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