Drug Repositioning for Non-Small Cell Lung Cancer by Using Machine Learning Algorithms and Topological Graph Theory

ASIAIR > College of Computer Science > Department of Biomedical informatics > Journal Article > Item 310904400/99866

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/99866

Title:	Drug Repositioning for Non-Small Cell Lung Cancer by Using Machine Learning Algorithms and Topological Graph Theory
Authors:	Huang), 黃建宏(Chien-Hung;Chang), Peter Mu-Hsi(Peter Mu-Hsin;Hsu), Chia-Wei Hsu(Chia-Wei;Huang), Chi-Ying Hua(Chi-Ying;吳家樂(Ng, Ka-Lok)*
Contributors:	生物資訊與醫學工程學系
Date:	2016-01
Issue Date:	2016-08-08 03:16:13 (UTC+0)
Abstract:	Background Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. Results This work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. Conclusions With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs. Keywords: Non-small cell lung cancer, Drug repositioning, Microarray data analysis, Machine learning algorithm, Topological parameters, Protein-protein interactions, Enrichment analysis, Connectivity Map
Relation:	BMC BIOINFORMATICS
Appears in Collections:	[Department of Biomedical informatics ] Journal Article

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