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Network-aided Bi-Clustering for discovering cancer subtypes
- Source :
- Scientific Reports, Vol 7, Iss 1, Pp 1-15 (2017), Scientific Reports
- Publication Year :
- 2017
- Publisher :
- Nature Portfolio, 2017.
-
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.
- Subjects :
- 0301 basic medicine
Science
0206 medical engineering
02 engineering and technology
Computational biology
Biology
computer.software_genre
Column (database)
Article
Matrix decomposition
03 medical and health sciences
Gene interaction
Neoplasms
Gene expression
medicine
Cluster Analysis
Humans
Gene Regulatory Networks
Protein Interaction Maps
Gene
Multidisciplinary
Gene Expression Profiling
Computational Biology
Cancer
medicine.disease
Expression (mathematics)
030104 developmental biology
Categorization
Medicine
Data mining
computer
020602 bioinformatics
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 7
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....d1e28ed2d680fb796a5404accf9572f4