ArticleComputational and structural biotechnology journal2020
An integrative multi-omics network-based approach identifies key regulators for breast cancer.
Article in Computational and structural biotechnology journal, 2020. 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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Who cites it
11 citing papers in PubMed.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 2024Article
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach.Biology · 2023Article
- A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer.BMC medical imaging · 2022Article
- Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer.International journal of molecular sciences · 2022Review
- Breast cancer in the era of integrating "Omics" approaches.Oncogenesis · 2022Review
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer.Frontiers in genetics · 2022Article
- Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer.Cancers · 2021Article
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Authors and funding
11 authors.
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Abstract
Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes,
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