IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

ABSTRACT:

Motivation

A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction.

Results

In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the fe

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10491952 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature