A comprehensive study of a svm-based mirna target prediction algorithm

Hui Liu, Dong Yue, Yidong Chen, Yufei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

MicroRNAs are single-stranded non-coding RNAs that play important regulatory roles in many biological processes and diseases. Identifying miRNA regulatory targets is paramount in elucidating its function. We carried out a comprehensive study of a new SYM-based target prediction algorithm called SYMicrO in this paper. The training data set is carefully derived from the most up-to-date collection of verified target s and multiple microarray data sets. Several varieties of feature design and selection schemes are investigated. The prediction results are compared with most of the existing algorithms, which show improved sensitivity and specificity of this two-stage SYM algorithm.

Original languageEnglish (US)
Title of host publication2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
DOIs
StatePublished - 2009
Event2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009 - Minneapolis, MN, United States
Duration: May 17 2009May 21 2009

Publication series

Name2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009

Other

Other2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
CountryUnited States
CityMinneapolis, MN
Period5/17/095/21/09

ASJC Scopus subject areas

  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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