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Analysis of breast cancer subtypes by AP-ISA biclustering

Overview of attention for article published in BMC Bioinformatics, November 2017
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Title
Analysis of breast cancer subtypes by AP-ISA biclustering
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1926-z
Pubmed ID
Authors

Liying Yang, Yunyan Shen, Xiguo Yuan, Junying Zhang, Jianhua Wei

Abstract

Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear. In this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies. Aiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples. Comparison with other methods validates the effectiveness of AP-ISA. New genes that would be useful for targeted treatment of breast cancer were also obtained in this study.

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Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Lecturer 3 15%
Student > Bachelor 2 10%
Librarian 1 5%
Other 1 5%
Other 2 10%
Unknown 6 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 15%
Agricultural and Biological Sciences 3 15%
Medicine and Dentistry 3 15%
Computer Science 2 10%
Nursing and Health Professions 1 5%
Other 1 5%
Unknown 7 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 November 2017.
All research outputs
#20,451,991
of 23,007,887 outputs
Outputs from BMC Bioinformatics
#6,889
of 7,315 outputs
Outputs of similar age
#283,420
of 325,276 outputs
Outputs of similar age from BMC Bioinformatics
#137
of 162 outputs
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