Cargando…

Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

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 a...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Chien-Hung, Chang, Peter Mu-Hsin, Hsu, Chia-Wei, Huang, Chi-Ying F., Ng, Ka-Lok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895785/
https://www.ncbi.nlm.nih.gov/pubmed/26817825
http://dx.doi.org/10.1186/s12859-015-0845-0
_version_ 1782435923336626176
author Huang, Chien-Hung
Chang, Peter Mu-Hsin
Hsu, Chia-Wei
Huang, Chi-Ying F.
Ng, Ka-Lok
author_facet Huang, Chien-Hung
Chang, Peter Mu-Hsin
Hsu, Chia-Wei
Huang, Chi-Ying F.
Ng, Ka-Lok
author_sort Huang, Chien-Hung
collection PubMed
description 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 IC(50) and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC(50) 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.
format Online
Article
Text
id pubmed-4895785
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-48957852016-06-10 Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory Huang, Chien-Hung Chang, Peter Mu-Hsin Hsu, Chia-Wei Huang, Chi-Ying F. Ng, Ka-Lok BMC Bioinformatics Proceedings 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 IC(50) and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC(50) 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. BioMed Central 2016-01-11 /pmc/articles/PMC4895785/ /pubmed/26817825 http://dx.doi.org/10.1186/s12859-015-0845-0 Text en © Huang et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Proceedings
Huang, Chien-Hung
Chang, Peter Mu-Hsin
Hsu, Chia-Wei
Huang, Chi-Ying F.
Ng, Ka-Lok
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
title Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
title_full Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
title_fullStr Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
title_full_unstemmed Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
title_short Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
title_sort drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895785/
https://www.ncbi.nlm.nih.gov/pubmed/26817825
http://dx.doi.org/10.1186/s12859-015-0845-0
work_keys_str_mv AT huangchienhung drugrepositioningfornonsmallcelllungcancerbyusingmachinelearningalgorithmsandtopologicalgraphtheory
AT changpetermuhsin drugrepositioningfornonsmallcelllungcancerbyusingmachinelearningalgorithmsandtopologicalgraphtheory
AT hsuchiawei drugrepositioningfornonsmallcelllungcancerbyusingmachinelearningalgorithmsandtopologicalgraphtheory
AT huangchiyingf drugrepositioningfornonsmallcelllungcancerbyusingmachinelearningalgorithmsandtopologicalgraphtheory
AT ngkalok drugrepositioningfornonsmallcelllungcancerbyusingmachinelearningalgorithmsandtopologicalgraphtheory