Dissecting cancer heterogeneity based on dimension reduction of transcriptomic profiles using extreme learning machines

It is becoming increasingly clear that major malignancies such as breast, colorectal and gastric cancers are not single disease entities, but comprising multiple cancer subtypes of distinct molecular properties. Molecular subtyping has been widely used to dissect inter-tumor biological heterogeneity...

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Bibliographic Details
Published in:PloS one 2018-09, Vol.13 (9), p.e0203824-e0203824
Main Authors: Wang, Kejun, Duan, Xin, Gao, Feng, Wang, Wei, Liu, Liangliang, Wang, Xin
Format: Article
Language:English
Subjects:
Algorithms
Automation
Bioinformatics
Biology and Life Sciences
Cancer
Classification
Clinical outcomes
Clustering
Colorectal cancer
Computer and Information Sciences
DNA methylation
Gastric cancer
Gene expression
Genomes
Genomics
Heterogeneity
Medical prognosis
Medicine and Health Sciences
Neural networks
Ovarian cancer
Ovarian carcinoma
Physical Sciences
Redundancy
Research and Analysis Methods
Social Sciences
Stomach cancer
Tumors
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Summary:It is becoming increasingly clear that major malignancies such as breast, colorectal and gastric cancers are not single disease entities, but comprising multiple cancer subtypes of distinct molecular properties. Molecular subtyping has been widely used to dissect inter-tumor biological heterogeneity, in relation to clinical outcomes. A key step of this methodology is to perform unsupervised classification of gene expression profiles, which, however, often suffers challenges of high-dimensionality, feature redundancy as well as noise and irrelevant information. To overcome these limitations, we propose ELM-CC, which employs hidden observation features obtained from extreme learning machines (ELMs) for cancer classification. To demonstrate the effectiveness and usefulness, we applied ELM-CC for gastric and ovarian cancer subtyping. Comparing with the widely-used consensus clustering method, our approach demonstrated much better clustering performance and identified molecular subtypes that are much more clinically relevant.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0203824