ArticleScientific reports2017
Network-aided Bi-Clustering for discovering cancer subtypes.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Semantic locality-aware biclustering for brain functional network connectivity.Scientific reports · 2025Article
- Unsupervised Algorithms for Microarray Sample Stratification.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Network Approaches for Precision Oncology.Advances in experimental medicine and biology · 2022Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- Article
- Bayesian generalized biclustering analysis via adaptive structured shrinkage.Biostatistics (Oxford, England) · 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
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bi-clustering is a widely used data mining technique for analyzing gene expression data. It simultaneously groups genes and samples of an input gene expression data matrix to discover bi-clusters that relevant samples exhibit similar gene expression profiles over a subset of genes. The discovered bi-clusters bring insights for categorization of cancer subtypes, gene treatments and others. Most existing bi-clustering approaches can only enumerate bi-clusters with constant values. Gene interaction networks can help to understand the pattern of cancer subtypes, but they are rarely integrated with gene expression data for exploring cancer subtypes. In this paper, we propose a novel method called Network-aided Bi-Clustering (NetBC). NetBC assigns weights to genes based on the structure of gene interaction network, and it iteratively optimizes sum-squared residue to obtain the row and column indicative matrices of bi-clusters by matrix factorization. NetBC can not only efficiently discover bi-clusters with constant values, but also bi-clusters with coherent trends. Empirical study on large-scale cancer gene expression datasets demonstrates that NetBC can more accurately discover cancer subtypes than other related algorithms.
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