title: Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.
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.
authors.affiliation: Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, 63205, Taiwan. chhuang@nfu.edu.tw.
authors.forename: Chien-Hung
authors.initials: CH
authors.lastname: Huang
authors.affiliation: Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital; Faculty of Medicine, National Yang Ming University, Taipei, 112, Taiwan. ptchang@vghtpe.gov.tw.
authors.forename: Peter Mu-Hsin
authors.initials: PM
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, 63205, Taiwan. 10163138@gm.nfu.edu.tw.
authors.forename: Chia-Wei
authors.initials: CW
authors.lastname: Hsu
authors.affiliation: Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, 112, Taiwan. cyhuang5@ym.edu.tw.
authors.forename: Chi-Ying F
authors.initials: CY
authors.lastname: Huang
authors.affiliation: Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan. ppiddi@gmail.com.|Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 40402, Taiwan. ppiddi@gmail.com.
authors.forename: Ka-Lok
authors.initials: KL
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy* / Q000235:genetics* / Q000473:pathology; D055808:Drug Discovery; D058492:Drug Repositioning; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000473:pathology; D000069550:Machine Learning; D046228:Microarray Analysis; D008962:Models, Theoretical; D009363:Neoplasm Proteins / Q000235:genetics*; D015398:Signal Transduction
chemical_list: D000970:Antineoplastic Agents; D009363:Neoplasm Proteins
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