A Lasso regression model for the construction of microRNA-target regulatory networks

Lu, Yiming; Zhou, Yang; Qu, Wubin; Deng, Minghua; Zhang, Chenggang

doi:10.1093/bioinformatics/btr410

Cited by 114 publications

(98 citation statements)

References 54 publications

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“…Due to high prediction accuracy and interpretability of the model based on LASSO, LASSO is often used to predict regression coefficients between miRNAs and mRNAs [15]. Let t ∈ {1, 2, .…”

Section: Lassomentioning

confidence: 99%

Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network

Yang

Chu

2017

Algorithms

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Abstract:The detection of composite miRNA functional module (CMFM) is of tremendous significance and helps in understanding the organization, regulation and execution of cell processes in cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we propose a novel module detection method called MBCFM (detecting Composite Function Modules based on Maximal Biclique enumeration), specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of multiple biological interaction information and topical network features. In this method, we employ algorithm MICA to enumerate all maximal bicliques and further extract R-pairs from the miRNA-mRNA regulatory network. Compared with two existing methods, Mirsynergy and SNMNMF on ovarian cancer dataset, the proposed method of MBCFM is not only able to extract cohesiveness-preserved CMFMs but also has high efficiency in running time. More importantly, MBCFM can be applied to detect other cancer-associated miRNA functional modules.

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“…Due to high prediction accuracy and interpretability of the model based on LASSO, LASSO is often used to predict regression coefficients between miRNAs and mRNAs [15]. Let t ∈ {1, 2, .…”

Section: Lassomentioning

confidence: 99%

Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network

Yang

Chu

2017

Algorithms

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show abstract

“…It has been applied to many bioinformatics domains, such as gene regulation network analysis [82] and microRNA-target regulatory network construction [83]. The L 1 constraint forces the weights of some features to exactly zero [82], and hence LASSO can automatically selects relevant features.…”

Section: Multi-label Lassomentioning

confidence: 99%

mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

Wan

Mak

Kung

2015

Journal of Theoretical Biology

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“…Commonly used methods for this task are based on RNA-RNA interaction prediction [101,102] or machine learning [103]. In this context, paired miRNA-mRNA expression profiling has proved to have great potential for the identification of novel interactions [104][105][106].…”

Section: Mirna Expression Datamentioning

confidence: 99%

Detecting Dysregulated Processes and Pathways

Stöckel

Lenhof

2014

Nucleic Acids as Molecular Diagnostics

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A Lasso regression model for the construction of microRNA-target regulatory networks

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 114 publications

References 54 publications

Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network

Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network

mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

Detecting Dysregulated Processes and Pathways

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