Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

ABSTRACT:

Background

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.

Principal findings

Three learning techniques were

SUBMITTER: McQuisten KA 

PROVIDER: S-EPMC2760777 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature