Computational methods to dissect gene regulatory networks in cancer
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Summary
Diverse computational methodologies that have sought to interpret somatic alterations and gene expression data through models of gene regulatory networks are reviewed to help identify patient populations who will benefit from therapy.
- Type
- article
- Published
- 2017-04-01
- Cited by
- 14
- References
- 60
- OpenAlex
- https://openalex.org/W2607461673
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:90499848
Keywords
Epigenomics, Gene regulatory network, Biology, Computational biology, Genomics
References
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- Multi-platform analysis of 12 cancer types reveals molecular classification within and across tissues-of-origin
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- Cancer Systems Biology: a peak into the future of patient care?
- Architecture of the human regulatory network derived from ENCODE data
- Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks
- Relative impact of nucleotide and copy number variation on gene expression phenotypes
- Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors
- Mutational landscape and significance across 12 major cancer types
- Discovery of Mutated Subnetworks Associated with Clinical Data in Cancer
- Defining an Essential Transcription Factor Program for Naïve Pluripotency
- Cancer Genome Landscapes
- Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy.
- Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information
Cited by
- Semi-supervised network inference using simulated gene expression dynamics
- Time-lagged Ordered Lasso for network inference
- Computational Modeling of Drug Response and Pathway Activity in Colorectal Cancer with Missing Data Reconstruction and Subtype Analysis
- Forest structure in epigenetic landscapes
- Large-scale datasets uncovering cell signalling networks in cancer: context matters.
- Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk
- ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages
- COSIFER: a Python package for the consensus inference of molecular interaction networks
- Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data
- Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data
- Identification of Therapeutic Gene Targets in Triple-Negative Breast Cancer: A Hybrid Approach Integrating Semidefinite Programming, Boolean Simulation, and Druggability Analysis
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data
- STAN, a computational framework for inferring spatially informed transcription factor activity
- ECMarker: Interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages
- Gene regulatory and biomolecular networks and their multifaceted biotechnological applications
- Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data
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