ArticleBMC bioinformatics2016
Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.Forestry research · 2025Article
- Reprogramming of cis-regulatory networks during skeletal muscle atrophy in male mice.Nature communications · 2023Article
- TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization.NAR genomics and bioinformatics · 2023Article
- An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method.Bioinformatics and biology insights · 2023Article
- Construction of a Hierarchical Gene Regulatory Network to Reveal the Drought Tolerance Mechanism of Shanxin Poplar.International journal of molecular sciences · 2022Article
- Two high hierarchical regulators, PuMYB40 and PuWRKY75, control the low phosphorus driven adventitious root formation in Populus ussuriensis.Plant biotechnology journal · 2022Article
- Current status and trends in forest genomics.Forestry research · 2022Review
- 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
- PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data.BMC bioinformatics · 2018Article
- TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction.Nucleic acids research · 2018Article
- Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways.PloS one · 2017Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMultilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.
resultsA bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically.
conclusionsWe validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.
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