Research Papers:

Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach

Bin Zhou _, Qi Sun and De-Xin Kong

PDF  |  HTML  |  Supplementary Files  |  How to cite

Oncotarget. 2016; 7:32394-32407. https://doi.org/10.18632/oncotarget.8716

Metrics: PDF 1715 views  |   HTML 2645 views  |   ?  


Bin Zhou1,2,*, Qi Sun1,2,*, De-Xin Kong1,2

1State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China

2Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

*These authors contributed equally to this work

Correspondence to:

De-Xin Kong, email: [email protected].

Keywords: chemoinformatics, cancer, cell line, drug development, similarity ensemble approach

Received: October 26, 2015     Accepted: March 28, 2016     Published: April 13, 2016


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.

Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 8716