ArticleBMC bioinformatics2014
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Identification of the methotrexate resistance-related diagnostic markers in osteosarcoma via adaptive total variation netNMF and multi-omics datasets.Frontiers in genetics · 2023Article
- Network-based cancer heterogeneity analysis incorporating multi-view of prior information.Bioinformatics (Oxford, England) · 2022Article
- DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network.Iranian journal of biotechnology · 2022Article
- Novel cancer subtyping method based on patient-specific gene regulatory network.Scientific reports · 2021Article
- NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer.Frontiers in genetics · 2021Article
- Gaussian Embedding for Large-scale Gene Set Analysis.Nature machine intelligence · 2020Article
- Bayesian generalized biclustering analysis via adaptive structured shrinkage.Biostatistics (Oxford, England) · 2020Article
- A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.Scientific reports · 2020Article
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- Knowledge-Guided Biclustering via Sparse Variational EM Algorithm.10th IEEE International Conference on Big Knowledge : proceedings : 10-11 November 2019, Beijing, China. IEEE International Conference on Big Knowledge (10th : 2019 : Beijing, China) · 2019Article
- Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology.JCO precision oncology · 2019Review
- It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.Briefings in bioinformatics · 2019Review
- miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase.BMC bioinformatics · 2018Article
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- Towards a holistic and solution-oriented monitoring of chemical status of European water bodies: how to support the EU strategy for a non-toxic environment?Environmental sciences Europe · 2018Article
- Network-aided Bi-Clustering for discovering cancer subtypes.Scientific reports · 2017Article
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Authors and funding
5 authors.
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Abstract
backgroundCancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering) to identify subtypes based on gene expression data. The network-level interaction among genes, which is key to understanding the molecular perturbations in cancer, has been rarely considered during the clustering process. The motivation of our work is to develop a method that effectively incorporates molecular interaction networks into the clustering process to improve cancer subtype identification.
resultsWe have developed a new clustering algorithm for cancer subtype identification, called "network-assisted co-clustering for the identification of cancer subtypes" (NCIS). NCIS combines gene network information to simultaneously group samples and genes into biologically meaningful clusters. Prior to clustering, we assign weights to genes based on their impact in the network. Then a new weighted co-clustering algorithm based on a semi-nonnegative matrix tri-factorization is applied. We evaluated the effectiveness of NCIS on simulated datasets as well as large-scale Breast Cancer and Glioblastoma Multiforme patient samples from The Cancer Genome Atlas (TCGA) project. NCIS was shown to better separate the patient samples into clinically distinct subtypes and achieve higher accuracy on the simulated datasets to tolerate noise, as compared to consensus hierarchical clustering.
conclusionsThe weighted co-clustering approach in NCIS provides a unique solution to incorporate gene network information into the clustering process. Our tool will be useful to comprehensively identify cancer subtypes that would otherwise be obscured by cancer heterogeneity, using high-throughput and high-dimensional gene expression data.
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