ArticleCancer informatics2014
Normalized lmQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers.
Article in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
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- High fat diet remodels the gene regulatory networks in the preoptic area.Scientific reports · 2026Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data.NPJ precision oncology · 2024Article
- A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.BMC genomics · 2023Article
- Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score.Journal of Alzheimer's disease : JAD · 2023Article
- Identify potential driver genes for PAX-FOXO1 fusion-negative rhabdomyosarcoma through frequent gene co-expression network mining.Frontiers in oncology · 2023Article
- Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis.Human genomics · 2022Article
- SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.BMC bioinformatics · 2022Article
- Distance correlation application to gene co-expression network analysis.BMC bioinformatics · 2022Article
- Workflow to Mine Frequent DNA Co-methylation Clusters in DNA Methylome Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Identification and validation of immune cells and hub genes alterations in recurrent implantation failure: A GEO data mining study.Frontiers in genetics · 2022Article
- Workflow to Mine Frequent DNA Co-methylation Clusters in DNA Methylome Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.Genomics, proteomics & bioinformatics · 2021Article
- TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery.BMC bioinformatics · 2021Article
- K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks.Genes · 2021Article
- Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer's Disease.Scientific reports · 2021Article
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2 authors.
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Abstract
In this paper, we present a new approach for mining weighted networks to identify densely connected modules such as quasi-cliques. Quasi-cliques are densely connected subnetworks in a network. Detecting quasi-cliques is an important topic in data mining, with applications such as social network study and biomedicine. Our approach has two major improvements upon previous work. The first is the use of local maximum edges to initialize the search in order to avoid excessive overlaps among the modules, thereby greatly reducing the computing time. The second is the inclusion of a weight normalization procedure to enable discovery of "subtle" modules with more balanced sizes. We carried out careful tests on multiple parameters and settings using two large cancer datasets. This approach allowed us to identify a large number of gene modules enriched in both biological functions and chromosomal bands in cancer data, suggesting potential roles of copy number variations (CNVs) involved in the cancer development. We then tested the genes in selected modules with enriched chromosomal bands using The Cancer Genome Atlas data, and the results strongly support our hypothesis that the coexpression in these modules are associated with CNVs. While gene coexpression network analyses have been widely adopted in disease studies, most of them focus on the functional relationships of coexpressed genes. The relationship between coexpression gene modules and CNVs are much less investigated despite the potential advantage that we can infer from such relationship without genotyping data. Our new approach thus provides a means to carry out deep mining of the gene coexpression network to obtain both functional and genetic information from the expression data.
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