Cargando…

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

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 thr...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Hongyu, Jiang, Limin, Tang, Jijun, Ding, Yijie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7982914/
https://www.ncbi.nlm.nih.gov/pubmed/33763416
http://dx.doi.org/10.3389/fcell.2021.615747
_version_ 1783667823532310528
author Zhang, Hongyu
Jiang, Limin
Tang, Jijun
Ding, Yijie
author_facet Zhang, Hongyu
Jiang, Limin
Tang, Jijun
Ding, Yijie
author_sort Zhang, Hongyu
collection PubMed
description 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).
format Online
Article
Text
id pubmed-7982914
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-79829142021-03-23 An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data Zhang, Hongyu Jiang, Limin Tang, Jijun Ding, Yijie Front Cell Dev Biol Cell and Developmental Biology 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). Frontiers Media S.A. 2021-03-05 /pmc/articles/PMC7982914/ /pubmed/33763416 http://dx.doi.org/10.3389/fcell.2021.615747 Text en Copyright © 2021 Zhang, Jiang, Tang and Ding. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Zhang, Hongyu
Jiang, Limin
Tang, Jijun
Ding, Yijie
An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data
title An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data
title_full An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data
title_fullStr An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data
title_full_unstemmed An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data
title_short An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data
title_sort accurate tool for uncovering cancer subtypes by fast kernel learning method to integrate multiple profile data
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7982914/
https://www.ncbi.nlm.nih.gov/pubmed/33763416
http://dx.doi.org/10.3389/fcell.2021.615747
work_keys_str_mv AT zhanghongyu anaccuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT jianglimin anaccuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT tangjijun anaccuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT dingyijie anaccuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT zhanghongyu accuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT jianglimin accuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT tangjijun accuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata
AT dingyijie accuratetoolforuncoveringcancersubtypesbyfastkernellearningmethodtointegratemultipleprofiledata