ArticleBMC bioinformatics2016
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 38 citations in OpenAlex.
- The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction.Journal of cheminformatics · 2025Article
- A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes.Biomedicines · 2023Article
- OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.Frontiers in genetics · 2023Article
- Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance.Bioengineering (Basel, Switzerland) · 2022Review
- Lung adenocarcinoma-related target gene prediction and drug repositioning.Frontiers in pharmacology · 2022Article
- Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning.Computational and mathematical methods in medicine · 2022Article
- Current progress and future perspectives of polypharmacology : From the view of non-small cell lung cancer.Seminars in cancer biology · 2021Review
- Vinculin expression in non-small cell lung cancer.The Journal of international medical research · 2020Article
- Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- The extraction of drug-disease correlations based on module distance in incomplete human interactome.BMC systems biology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-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.
resultsThis 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.
conclusionsWith 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.