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An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data

Authors :
Hongyu Zhang
Limin Jiang
Jijun Tang
Yijie Ding
Source :
Frontiers in Cell and Developmental Biology, Vol 9 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

Details

Language :
English
ISSN :
2296634X
Volume :
9
Database :
Directory of Open Access Journals
Journal :
Frontiers in Cell and Developmental Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.6e4cc91a3f4ec3b9708ef2379aabd2
Document Type :
article
Full Text :
https://doi.org/10.3389/fcell.2021.615747