maTE: discovering expressed interactions between microRNAs and their targets

Malik Yousef; Loai Abddallah; Jens Allmer

doi:10.1093/bioinformatics/btz204

maTE: discovering expressed interactions between microRNAs and their targets

Malik Yousef^*, Loai Abddallah, Jens Allmer

^*Corresponding author for this work

BIOS Applied Bioinformatics

Research output: Contribution to journal › Article › Academic › peer-review

17 Citations (Scopus)

Abstract

Motivation

Disease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. MicroRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression.

We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.

Original language	English
Article number	btz204
Pages (from-to)	4020-2028
Journal	Bioinformatics
Volume	35
Issue number	20
Early online date	21 Mar 2019
DOIs	https://doi.org/10.1093/bioinformatics/btz204
Publication status	Published - 15 Oct 2019

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abstract = "MotivationDisease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. MicroRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression.We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.",

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AB - MotivationDisease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. MicroRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression.We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.

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maTE: discovering expressed interactions between microRNAs and their targets

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