Paper Info
Title
Ranking of microRNA target prediction scores by Pareto front analysis.
Abstract
Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA–mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA–mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and – unlike standard thresholding methods – utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure, which encourages further research towards a higher-dimensional analysis of Pareto fronts.
Year
DOI
Venue
2010
10.1016/j.compbiolchem.2010.09.005
Computational Biology and Chemistry
Keywords
Field
DocType
MicroRNAs,Target mRNAs,Multi-objective optimisation,Pareto front
Data mining,Ranking,Multi-objective optimization,Proof of concept,Artificial intelligence,Thresholding,Bioinformatics,Machine learning,Pareto principle,Mathematics
Journal
Volume
Issue
ISSN
34
5
1476-9271
Citations 
PageRank 
References 
0
0.34
19
Authors
2
Name
Order
Citations
PageRank
Sudhakar Sahoo15113.13
Andreas A. Albrecht2232.96