Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
Language English Country Great Britain, England Media print-electronic
Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
Grant support
P30 CA016087
NCI NIH HHS - United States
PubMed
31374225
DOI
10.1016/j.pharmthera.2019.107395
PII: S0163-7258(19)30138-X
Knihovny.cz E-resources
- Keywords
- Association Rule Mining, Data mining, Drug Response Prediction, Machine Learning, Precision Medicine,
- MeSH
- Data Mining * MeSH
- Humans MeSH
- Neoplasms drug therapy MeSH
- Computer Simulation MeSH
- Machine Learning * MeSH
- Treatment Outcome MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- Research Support, N.I.H., Extramural MeSH
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
Applied Bioinformatics Laboratories NYU School of Medicine New York NY 10016 USA
Laboratory of Tumour Cell Biology School of Medicine University of Crete Heraklion Crete Greece
School of Mechanical Engineering National Technical University of Athens Zografou 15780 Greece
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