A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This manuscript provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction, tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies, including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.