ArticleScientific reports2015
Detection of gene communities in multi-networks reveals cancer drivers.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed.
- The cingulum as a marker of individual differences in neurocognitive development.Scientific reports · 2019Trial
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Engineered Living Systems With Self-Organizing Neural Networks: From Anatomy to Behavior and Gene Expression.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Basal Xenobot transcriptomics reveals changes and novel control modality in cells freed from organismal influence.Communications biology · 2025Article
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring weighted network backbone extraction: A comparative analysis of structural techniques.PloS one · 2025Article
- An integrative network-based approach to identify driving gene communities in chronic obstructive pulmonary disease.NPJ systems biology and applications · 2024Article
- Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS.Bioinformatics (Oxford, England) · 2024Article
- Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.Nature communications · 2024Article
- Genome-Wide Network Analysis of Above- and Below-Ground Co-growth inPlant phenomics (Washington, D.C.) · 2024Article
- Article
- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
- An evaluation tool for backbone extraction techniques in weighted complex networks.Scientific reports · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks.International journal of molecular sciences · 2023Article
- Hybrid neural network approaches to predict drug-target binding affinity for drug repurposing: screening for potential leads for Alzheimer's disease.Frontiers in molecular biosciences · 2023Article
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Article
- Integration of artificial intelligence and precision oncology in Latin America.Frontiers in medical technology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.
Indexed as
Identifiers
What OpenQuestion holds
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.