ArticleInternational journal of data mining and bioinformatics
Integration of multi-omics data for integrative gene regulatory network inference.
Article in International journal of data mining and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Circulating long noncoding RNA: New frontiers in biomarker research for mood disorders.Genomic psychiatry : advancing science from genes to society · 2025Article
- Donor-specific digital twin for living donor liver transplant recovery.Biology methods & protocols · 2025Article
- A Selective Review of Network Analysis Methods for Gene Expression Data.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Multi-Omics Integration in Nephrology: Advances, Challenges, and Future Directions.Seminars in nephrology · 2024Review
- scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.International journal of molecular sciences · 2023Article
- A guide to multi-omics data collection and integration for translational medicine.Computational and structural biotechnology journal · 2023Review
- Using empirical biological knowledge to infer regulatory networks from multi-omics data.BMC bioinformatics · 2022Article
- Review and assessment of Boolean approaches for inference of gene regulatory networks.Heliyon · 2022Review
- TiMEG: an integrative statistical method for partially missing multi-omics data.Scientific reports · 2021Article
- Molecular Mechanisms Associated with Antidepressant Treatment on Major Depression.Complex psychiatry · 2021Review
- Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance.Frontiers in genetics · 2021Article
- Transcriptome Reveals Allele Contribution to Heterosis in Maize.Frontiers in plant science · 2021Article
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- Subtypes identification on heart failure with preserved ejection fraction via network enhancement fusion using multi-omics data.Computational and structural biotechnology journal · 2021Article
- The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach.The EPMA journal · 2020Review
- Prospects and challenges of multi-omics data integration in toxicology.Archives of toxicology · 2020Review
- Network modeling of single-cell omics data: challenges, opportunities, and progresses.Emerging topics in life sciences · 2019Review
- Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.BMC systems biology · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.
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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.