Biomarker interaction selection and disease detection based on multivariate gain ratio

doi:10.1186/s12859-022-04699-7

CSpace

	Biomarker interaction selection and disease detection based on multivariate gain ratio
	Chu, Xiao 1; Jiang, Mao 1; Liu, Zhuo-Jun 2
	2022-05-12
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	23 期号:1 页码:16
摘要	Background: Disease detection is an important aspect of biotherapy. With the development of biotechnology and computer technology, there are many methods to detect disease based on single biomarker. However, biomarker does not influence disease alone in some cases. It's the interaction between biomarkers that determines disease status. The existing influence measure I-score is used to evaluate the importance of interaction in determining disease status, but there is a deviation about the number of variables in interaction when applying I-score. To solve the problem, we propose a new influence measure Multivariate Gain Ratio (MGR) based on Gain Ratio (GR) of single-variate, which provides us with multivariate combination called interaction. Results: We propose a preprocessing verification algorithm based on partial predictor variables to select an appropriate preprocessing method. In this paper, an algorithm for selecting key interactions of biomarkers and applying key interactions to construct a disease detection model is provided. MGR is more credible than I-score in the case of interaction containing small number of variables. Our method behaves better with average accuracy 93.13% than I-score of 91.73% in Breast Cancer Wisconsin (Diagnostic) Dataset. Compared to the classification results 89.80% based on all predictor variables, MGR identifies the true main biomarkers and realizes the dimension reduction. In Leukemia Dataset, the experiment results show the effectiveness of MGR with the accuracy of 97.32% compared to I-score with accuracy 89.11%. The results can be explained by the nature of MGR and I-score mentioned above because every key interaction contains a small number of variables in Leukemia Dataset. Conclusions: MGR is effective for selecting important biomarkers and biomarker interactions even in high-dimension feature space in which the interaction could contain more than two biomarkers. The prediction ability of interactions selected by MGR is better than I-score in the case of interaction containing small number of variables. MGR is generally applicable to various types of biomarker datasets including cell nuclei, gene, SNPs and protein datasets.
关键词	Multivariate gain ratio Biomarker interaction Disease detection
DOI	10.1186/s12859-022-04699-7
收录类别	SCI
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000794881400002
出版者	BMC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/61298
专题	中国科学院数学与系统科学研究院
通讯作者	Chu, Xiao
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chu, Xiao,Jiang, Mao,Liu, Zhuo-Jun. Biomarker interaction selection and disease detection based on multivariate gain ratio[J]. BMC BIOINFORMATICS,2022,23(1):16.
APA	Chu, Xiao,Jiang, Mao,&Liu, Zhuo-Jun.(2022).Biomarker interaction selection and disease detection based on multivariate gain ratio.BMC BIOINFORMATICS,23(1),16.
MLA	Chu, Xiao,et al."Biomarker interaction selection and disease detection based on multivariate gain ratio".BMC BIOINFORMATICS 23.1(2022):16.