Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles

Highlights • A computational model based on random forest algorithm for identifying synergistic anti-cancer drug combinations is proposed. • The model is based on features of biology network and gene expression profiles. • Twenty-eight synergistic drug combinations were predicted, three out of which...

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Veröffentlicht in:	Artificial intelligence in medicine 2017-11, Vol.83, p.35-43
Hauptverfasser:	Li, Xiangyi, Xu, Yingjie, Cui, Hui, Huang, Tao, Wang, Disong, Lian, Baofeng, Li, Wei, Qin, Guangrong, Chen, Lanming, Xie, Lu
Format:	Artikel
Sprache:	eng
Schlagworte:	Antineoplastic Combined Chemotherapy Protocols - adverse effects Antineoplastic Combined Chemotherapy Protocols - therapeutic use Artificial Intelligence Cancer Computational Biology - methods Computer Simulation Databases, Genetic Drug Synergism Gene expression profiles Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Internal Medicine Neoplasms - drug therapy Neoplasms - genetics Neoplasms - metabolism Neoplasms - pathology Network features Other Pharmacogenetics Protein Interaction Maps Random forest Reproducibility of Results Signal Transduction - drug effects Synergistic drug combinations Transcriptome - drug effects
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Beschreibung
Zusammenfassung:	Highlights • A computational model based on random forest algorithm for identifying synergistic anti-cancer drug combinations is proposed. • The model is based on features of biology network and gene expression profiles. • Twenty-eight synergistic drug combinations were predicted, three out of which were reported, and the rest could be candidates for further investigations.
ISSN:	0933-3657 1873-2860
DOI:	10.1016/j.artmed.2017.05.008