ReviewBriefings in bioinformatics2025
Machine learning methods for gene regulatory network inference.
Review in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A causal reinforcement learning framework for reliable gene regulatory network inference.BMC bioinformatics · 2026Article
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Brain Cancer: Molecular Alterations and Emerging Trends in Neuropharmacology.International journal of molecular sciences · 2026Review
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- Stage-Specific Reconstruction of Genome-Wide Genetic and Epigenetic Regulatory Networks Reveals Mechanistic Insights into Asthma Progression.International journal of molecular sciences · 2026Article
- Analysis of biological networks using Krylov subspace trajectories.bioRxiv : the preprint server for biology · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Data-driven strategies for immunoradiotherapy in uveal melanoma: the role of artificial intelligence.Frontiers in pharmacology · 2026Review
- Post-transcriptional regulation of light-stress responses and predictive modeling in vegetableFrontiers in plant science · 2026Review
- scGraphVerse: a modular workflow for single-cell gene network inference.Bioinformatics advances · 2026Article
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene Regulatory Networks (GRNs) are intricate biological systems that control gene expression and regulation in response to environmental and developmental cues. Advances in computational biology, coupled with high-throughput sequencing technologies, have significantly improved the accuracy of GRN inference and modeling. Modern approaches increasingly leverage artificial intelligence (AI), particularly machine learning techniques-including supervised, unsupervised, semi-supervised, and contrastive learning-to analyze large-scale omics data and uncover regulatory gene interactions. To support both the application of GRN inference in studying gene regulation and the development of novel machine learning methods, we present a comprehensive review of machine learning-based GRN inference methodologies, along with the datasets and evaluation metrics commonly used. Special emphasis is placed on the emerging role of cutting-edge deep learning techniques in enhancing inference performance. The major challenges and potential future directions for improving GRN inference are also discussed.
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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.