Stable solution to l 2,1 -based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases

BMC Medical Genomics
Ashis Kumer BiswasJean X Gao

Abstract

A large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities through biological experiments are expensive. However, a plethora lincRNA-data are available now, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc, which opens the opportunity for cutting-edge machine learning and data mining approaches to extract meaningful relationships among lincRNAs and diseases. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of them utilizes side information of both the entities simultaneously in a single framework. The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information about them. However, the formulation of IMC is incapable of handling noise and outliers that may be present in the datasets, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve the two issues. As a remedy, in...Continue Reading

References

Nov 21, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chris DingMichael I Jordan
Jul 16, 2010·Nature Reviews. Genetics·Roger P AlexanderMark B Gerstein
Nov 19, 2011·Nature Reviews. Genetics·Manel Esteller
Nov 24, 2012·Nucleic Acids Research·Geng ChenQinghua Cui
Jun 17, 2014·Bioinformatics·Nagarajan Natarajan, Inderjit S Dhillon
Jan 17, 2015·IEEE Transactions on Nanobioscience·Gamage Upeksha GanegodaQilong Feng

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

Lin2GO
TsLincRNA
RWRH
RIMC
lincRNA
Disease
Linc2GO
LRLSLDA
OMIM API

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