Co-clustering algorithm for the identification of cancer subtypes from gene expression data
نویسندگان
چکیده
منابع مشابه
Hybrid Algorithm for Clustering Gene Expression Data
Microarray gene expressions provide an insight into genomic biomarkers that aid in identifying cancerous cells and normal cells. In this study, functionally related genes are identified by partitioning gene data. Clustering is an unsupervised learning technique that partition gene data into groups based on the similarity between their expression profiles. This identifies functionally related ge...
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In many applications, the expert interpretation of coclustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a collection of co-clusters: each co-cluster is a group of objects associated to a group of attributes and these associations can support interpretations. Many constrained clustering algorithms have been proposed to exploit the do...
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Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profile...
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Introduction: HER2-enriched subtype of breast cancer has a worse prognosis than luminal subtypes. Recently, the discovery of targeted therapies in other groups of breast cancer has increased patient survival. The aim of this study was to identify genes that affect the overall survival of this group of patients based on a systems biology approach. Methods: Gene expression data and clinical infor...
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A new framework is proposed to study the co-clustering of gene expression data. This framework is based on a generic tensor optimization model and an optimization method termed Maximum Block Improvement (MBI) recently developed in [3]. Not only can this framework be applied for co-clustering gene expression data with genes expressed at different conditions represented in 2D matrices, but it can...
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ژورنال
عنوان ژورنال: TELKOMNIKA (Telecommunication Computing Electronics and Control)
سال: 2019
ISSN: 2302-9293,1693-6930
DOI: 10.12928/telkomnika.v17i4.12773