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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Gene regulatory networks (GRNs) are fundamental to cellular processes.
• Comprehensive GRN maps are crucial for understanding gene function and identifying candidate genes.
• High-throughput gene expression data has enabled the development of computational methods for GRN inference.

Purpose of the Study:

• To review recent advancements in computational inference of GRNs using plant transcriptome data.
• To highlight strategies for selecting contextual genes and methods for GRN inference.
• To discuss challenges and opportunities in GRN elucidation from large-scale, emerging transcriptomic datasets.

Main Methods:

• Summary of statistical and machine learning (ML)-based algorithms for GRN inference.
• Review of strategies for contextual gene selection in GRN construction.
• Analysis of GRN inference from large-scale plant transcriptome sequencing datasets.

Main Results:

• Recent statistical and ML methods show significant progress in GRN construction.
• Contextual gene selection and advanced algorithms improve GRN inference accuracy.
• Emerging transcriptomic applications offer new avenues for GRN analysis.

Conclusions:

• Computational inference of GRNs from plant transcriptome data is rapidly advancing.
• Large-scale and novel transcriptomic data analyses present opportunities for deeper biological insights.
• Effective GRN maps are vital for functional genomics and crop improvement.