Cargando…
miTarget: microRNA target gene prediction using a support vector machine
BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been p...
Autores principales: | , , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2006
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1594580/ https://www.ncbi.nlm.nih.gov/pubmed/16978421 http://dx.doi.org/10.1186/1471-2105-7-411 |
_version_ | 1782130431971295232 |
---|---|
author | Kim, Sung-Kyu Nam, Jin-Wu Rhee, Je-Keun Lee, Wha-Jin Zhang, Byoung-Tak |
author_facet | Kim, Sung-Kyu Nam, Jin-Wu Rhee, Je-Keun Lee, Wha-Jin Zhang, Byoung-Tak |
author_sort | Kim, Sung-Kyu |
collection | PubMed |
description | BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. RESULTS: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. CONCLUSION: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples. |
format | Text |
id | pubmed-1594580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-15945802006-10-12 miTarget: microRNA target gene prediction using a support vector machine Kim, Sung-Kyu Nam, Jin-Wu Rhee, Je-Keun Lee, Wha-Jin Zhang, Byoung-Tak BMC Bioinformatics Software BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. RESULTS: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. CONCLUSION: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples. BioMed Central 2006-09-18 /pmc/articles/PMC1594580/ /pubmed/16978421 http://dx.doi.org/10.1186/1471-2105-7-411 Text en Copyright © 2006 Kim et al; licensee BioMed Central Ltd. https://creativecommons.org/licenses/by/2.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Kim, Sung-Kyu Nam, Jin-Wu Rhee, Je-Keun Lee, Wha-Jin Zhang, Byoung-Tak miTarget: microRNA target gene prediction using a support vector machine |
title | miTarget: microRNA target gene prediction using a support vector machine |
title_full | miTarget: microRNA target gene prediction using a support vector machine |
title_fullStr | miTarget: microRNA target gene prediction using a support vector machine |
title_full_unstemmed | miTarget: microRNA target gene prediction using a support vector machine |
title_short | miTarget: microRNA target gene prediction using a support vector machine |
title_sort | mitarget: microrna target gene prediction using a support vector machine |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1594580/ https://www.ncbi.nlm.nih.gov/pubmed/16978421 http://dx.doi.org/10.1186/1471-2105-7-411 |
work_keys_str_mv | AT kimsungkyu mitargetmicrornatargetgenepredictionusingasupportvectormachine AT namjinwu mitargetmicrornatargetgenepredictionusingasupportvectormachine AT rheejekeun mitargetmicrornatargetgenepredictionusingasupportvectormachine AT leewhajin mitargetmicrornatargetgenepredictionusingasupportvectormachine AT zhangbyoungtak mitargetmicrornatargetgenepredictionusingasupportvectormachine |