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IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions
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 function...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491952/ https://www.ncbi.nlm.nih.gov/pubmed/37647643 http://dx.doi.org/10.1093/bioinformatics/btad530 |
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author | Liu, Yiwei Yang, Changhuo Li, Hong-Dong Wang, Jianxin |
author_facet | Liu, Yiwei Yang, Changhuo Li, Hong-Dong Wang, Jianxin |
author_sort | Liu, Yiwei |
collection | PubMed |
description | 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 feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION: IsoFrog is freely available at https://github.com/genemine/IsoFrog. |
format | Online Article Text |
id | pubmed-10491952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-104919522023-09-10 IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions Liu, Yiwei Yang, Changhuo Li, Hong-Dong Wang, Jianxin Bioinformatics Original Paper 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 feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION: IsoFrog is freely available at https://github.com/genemine/IsoFrog. Oxford University Press 2023-08-30 /pmc/articles/PMC10491952/ /pubmed/37647643 http://dx.doi.org/10.1093/bioinformatics/btad530 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Liu, Yiwei Yang, Changhuo Li, Hong-Dong Wang, Jianxin IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions |
title | IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions |
title_full | IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions |
title_fullStr | IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions |
title_full_unstemmed | IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions |
title_short | IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions |
title_sort | isofrog: a reversible jump markov chain monte carlo feature selection-based method for predicting isoform functions |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491952/ https://www.ncbi.nlm.nih.gov/pubmed/37647643 http://dx.doi.org/10.1093/bioinformatics/btad530 |
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