Identifying predictive features in drug response using machine learning: opportunities and challenges.
Journal:
Annual review of pharmacology and toxicology
Published Date:
Dec 12, 2014
Abstract
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.
Authors
Keywords
Algorithms
Animals
Antineoplastic Agents
Artificial Intelligence
Cluster Analysis
Drug Discovery
Drug-Related Side Effects and Adverse Reactions
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Neural Networks, Computer
Patient Safety
Patient Selection
Pattern Recognition, Automated
Pharmacology
Precision Medicine
Risk Assessment
Risk Factors