ArticleBioinformatics (Oxford, England)2018
Network-based integration of multi-omics data for prioritizing cancer genes.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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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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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
84 citing papers in PubMed, 172 citations in OpenAlex.
- Artificial intelligence and multiomics integration for Parkinson's disease drug development.Molecules and cells · 2026Review
- NCES: A Cell-Specific Network-Augmented Essentiality Framework for Cancer Therapeutic Target Discovery.Computational and structural biotechnology journal · 2026Article
- panomiX: Investigating mechanisms of trait emergence through multi-omics data integration.Plant phenomics (Washington, D.C.) · 2025Article
- scGT:integration algorithm for single-cell RNA-seq and ATAC-seq based on graph transformer.Bioinformatics (Oxford, England) · 2025Article
- Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data.BMC bioinformatics · 2025Article
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- MNMO: discover driver genes from a multi-omics data based-multi-layer network.Bioinformatics (Oxford, England) · 2025Article
- NPC1L1 on pancreatic adenocarcinoma cell functions as a two-pronged checkpoint against antitumor activity.Innovation (Cambridge (Mass.)) · 2025Article
- Identification of cancer driver genes based on hierarchical weak consensus model.Health information science and systems · 2024Article
- PONYTA: prioritization of phenotype-related genes from mouse KO events using PU learning on a biological network.Bioinformatics (Oxford, England) · 2024Article
- Review
- Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.Annual review of biomedical data science · 2024Review
- GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network.iScience · 2024Article
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
- Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.Current drug delivery · 2024Review
- GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.Bioinformatics (Oxford, England) · 2023Article
- Extending protein interaction networks using proteoforms and small molecules.Bioinformatics (Oxford, England) · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results: We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS' competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation: NetICS is available at https://github.com/cbg-ethz/netics. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.