Article Abstract

Artificial neural networks in the cancer genomics frontier

Authors: Andrew Oustimov, Vincent Vu


The advent of DNA-microarray and sequencing technology has enabled researchers to simultaneously measure the expression levels of thousands of genes, resulting in large amounts of potentially intriguing data, which requires careful, insightful, and robust analysis. Artificial neural networks (ANNs) facilitate fascinating analysis strategies capable of addressing many noisy, correlated inputs, while utilizing their parallel nature for the simultaneous detection of a multitude of subtle, yet pertinent features, thus allowing researchers to gain valuable knowledge regarding the cause, progression, and treatment of cancer. This paper is intended as an introduction to ANNs as they are utilized in cancer genomic studies, while simultaneously providing a brief survey of components that comprise the analysis of genomic data.