Profiled support vector machines for antisense oligonucleotide efficacy prediction

Background: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results: In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 ...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Gustavo Camps-Valls, Alistair M. Chalk, Antonio J. Serrano-López, José David Martín-Guerrero, Erik L. L. Sonnhammer
• Keywords: AO Prediction, BMCBI 2004, Profiled, Support Vector Machines