Mirsynergy: Detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion

Yue Li; Cheng Liang; Ka-Chun Wong; Jiawei Luo; Zhaolei Zhang

doi:10.1093/bioinformatics/btu373

Mirsynergy: Detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion

Yue Li, Cheng Liang, Ka-Chun Wong, Jiawei Luo, Zhaolei Zhang

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

79 Citations (Scopus)

Abstract

Motivation: Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.

Original language	English
Pages (from-to)	2627-2635
Journal	Bioinformatics
Volume	30
Issue number	18
DOIs	https://doi.org/10.1093/bioinformatics/btu373
Publication status	Published - 2014
Externally published	Yes

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title = "Mirsynergy: Detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion",

abstract = "Motivation: Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.",

author = "Yue Li and Cheng Liang and Ka-Chun Wong and Jiawei Luo and Zhaolei Zhang",

year = "2014",

doi = "10.1093/bioinformatics/btu373",

language = "English",

volume = "30",

pages = "2627--2635",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

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TY - JOUR

T1 - Mirsynergy

T2 - Detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion

AU - Li, Yue

AU - Liang, Cheng

AU - Wong, Ka-Chun

AU - Luo, Jiawei

AU - Zhang, Zhaolei

PY - 2014

Y1 - 2014

N2 - Motivation: Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.

AB - Motivation: Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84907483969&origin=recordpage

U2 - 10.1093/bioinformatics/btu373

DO - 10.1093/bioinformatics/btu373

M3 - RGC 21 - Publication in refereed journal

C2 - 24894504

SN - 1367-4803

VL - 30

SP - 2627

EP - 2635

JO - Bioinformatics

JF - Bioinformatics

IS - 18

ER -

Mirsynergy: Detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion

Abstract

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