Year: 2013
Type: article
Abstract: Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF) and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM), bagging, and random forest) on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression) increase the prediction accuracy as compared to using gene expression alone.
Authors: Hussein Hijazi, Christina Chan
Institution: Michigan State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,375
Awards: CDI-Type II: Discovery of Biophysical Mechanisms Inducing Signaling and Cytotoxicity: An Experimental Approach Enabled by Cyber Tools, Low Temperature Transcriptional Networks, G316389574, Develop a Dynamic Model that Incoporates Text-mining to Reconstruct Networks, G6490296102, Maximizing siRNA Function through Mechanism-based Sequence and Vehicle Design
OpenAlex ID: W2108589405