Derivative scores from site accessibility and ranking of miRNA target predictions

Authors:
Abu Z. M. Dayem Ullah;Sudhakar Sahoo;Kathleen Steinhöfel;Andreas A. Albrecht
Affiliations:
Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK;Molecular Diagnostics Laboratory, Aarhus University Hospital, Aarhus 8200, Denmark;Department of Informatics, King's College London, London WC2R 2LS, UK;Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast BT9 7BL, UK
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2012

Citing 6
Cited 0

Multiobjective Optimization in Bioinformatics and Computational Biology

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multicriteria gene screening for analysis of differential expression with DNA microarrays

EURASIP Journal on Applied Signal Processing
Naïve Bayes for microRNA target predictions—machine learning for microRNA targets

Bioinformatics
Prediction of both conserved and nonconserved microRNA targets in animals

Bioinformatics
Research Article: Computational identification of novel microRNA homologs in the chimpanzee genome

Computational Biology and Chemistry
Multi-objective tag SNPs selection using evolutionary algorithms

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the present study, we define derivative scoring functions from PITA and STarMir predictions. The scoring functions are evaluated for up to five selected miRNAs with a relatively large number of validated targets reported by TarBase and miRecords. The average ranking of validated targets returned by PITA and STarMir is compared to the average ranking produced by the new derivatives scores. We obtain an average improvement of 13.6% (STD∼5.7%) relative to the average ranking of validated targets produced by PITA and STarMir.