ArticleNucleic acids research2018
TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 28 citations in OpenAlex.
- Genome-wide identification and functional roles relating to anthocyanin biosynthesis analysis in maize.BMC plant biology · 2025Article
- Genome-Wide Identification and Characterization of Lignin Synthesis Genes in Maize.International journal of molecular sciences · 2024Article
- Regulation of regeneration inaBIOTECH · 2023Article
- TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization.NAR genomics and bioinformatics · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- Construction of a Hierarchical Gene Regulatory Network to Reveal the Drought Tolerance Mechanism of Shanxin Poplar.International journal of molecular sciences · 2022Article
- Transcriptional regulation of proanthocyanidin biosynthesis pathway genes and transcription factors in Indigofera stachyodes Lindl. roots.BMC plant biology · 2022Article
- TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles.Nucleic acids research · 2021Article
- Identification of biological pathway and process regulators using sparse partial least squares and triple-gene mutual interaction.Scientific reports · 2021Article
- HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression.Forestry research · 2021Article
- Transcriptional and Post-transcriptional Regulation of Lignin Biosynthesis Pathway Genes inFrontiers in plant science · 2020Review
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories.
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