title: Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach.
abstract: In this study, we proposed an improved algorithm for identifying proteins relevant to cancer. The algorithm was named two-layer molecular similarity ensemble approach (TL-SEA). We applied TL-SEA to analyzing the correlation between anticancer compounds (against cell lines K562, MCF7 and A549) and active compounds against separate target proteins listed in BindingDB. Several associations between cancer types and related proteins were revealed using this chemoinformatics approach. An analysis of the literature showed that 26 of 35 predicted proteins were correlated with cancer cell proliferation, apoptosis or differentiation. Additionally, interactions between proteins in BindingDB and anticancer chemicals were also predicted. We discuss the roles of the most important predicted proteins in cancer biology and conclude that TL-SEA could be a useful tool for inferring novel proteins involved in cancer and revealing underlying molecular mechanisms.
authors.affiliation: State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Zhou
authors.affiliation: State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Sun
authors.affiliation: State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: De-Xin
authors.initials: DX
authors.lastname: Kong
mesh_terms: D000072283:A549 Cells; D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology; D017209:Apoptosis; D014408:Biomarkers, Tumor / Q000378:metabolism*; D002454:Cell Differentiation; D049109:Cell Proliferation; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D020014:K562 Cells; D061986:MCF-7 Cells; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000188:drug therapy / Q000378:metabolism* / Q000473:pathology; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D015398:Signal Transduction
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins
keywords: cancer; cell line; chemoinformatics; drug development; similarity ensemble approach