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Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets
2018
Frontiers in Plant Science
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, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to
doi:10.3389/fpls.2018.01770
pmid:30555503
pmcid:PMC6281826
fatcat:phaqyh5odvf4nkcwrwiqstoz2i