ReviewFrontiers in plant science2018
Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets.
Review in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of biomarker genes from multiple studies for abiotic stress in maize through machine learning.Journal of biosciences · 2024Pooled it
- Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.Briefings in bioinformatics · 2025Article
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- Inferring cell-type-specific gene regulatory network from cellular transcriptomics data with GeneLink.Briefings in bioinformatics · 2025Article
- Inferring gene regulatory networks with graph convolutional network based on causal feature reconstruction.Scientific reports · 2024Article
- SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.Bioinformatics (Oxford, England) · 2024Article
- MICA: a multi-omics method to predict gene regulatory networks in early human embryos.Life science alliance · 2024Article
- Development and applications of metabolic models in plant multi-omics research.Frontiers in plant science · 2024Review
- Article
- Predicting rice diseases using advanced technologies at different scales: present status and future perspectives.aBIOTECH · 2023Review
- Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.PLoS computational biology · 2023Article
- Study on the Alleviating Effect and Potential Mechanism of Ethanolic Extract ofInternational journal of molecular sciences · 2023Article
- Classifying Circumnutation in Pea Plants via Supervised Machine Learning.Plants (Basel, Switzerland) · 2023Article
- Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae.Advances in nutrition (Bethesda, Md.) · 2023Article
- Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specificFrontiers in genetics · 2023Article
- Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks.Briefings in bioinformatics · 2022Article
- Global coordination level in single-cell transcriptomic data.Scientific reports · 2022Article
- GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.