Title

A Comprehensive Analysis Of Classification Algorithms For Cancer Prediction From Gene Expression

Document Type

Conference Proceeding

Publication Date

2015

Published In

Proceedings Of The 6th ACM Conference On Bioinformatics, Computational Biology And Health Informatics

Abstract

With the advent of inexpensive microarray technology, biologists have become increasingly reliant on gene expression analysis for detecting disease states, including diagnosis of cancerous tissue [12]. While random forests and SVMs have proven to be popular methods for expression analysis, little work has been done to compare these methods with AdaBoost, a popular ensemble learning algorithm, across a wide array of cancer prediction tasks. Our work shows AdaBoost outperforms other approaches on binary predictions while random forests and SVMs are the best choice in multi-class predictions.

Published By

ACM

Conference

6th ACM Conference On Bioinformatics, Computational Biology And Health Informatics

Conference Dates

September 9-12, 2015

Conference Location

Atlanta, GA