Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereb...

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Bibliographic Details
Main Authors: Keiichi Mochida, Satoru Koda, Komaki Inoue, Ryuei Nishii
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Plant Science
Subjects:
machine learning
gene regulatory network
sparse modeling
transcriptome
time series analysis
Online Access:https://www.frontiersin.org/article/10.3389/fpls.2018.01770/full

Internet

https://www.frontiersin.org/article/10.3389/fpls.2018.01770/full

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