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Disease category-specific annotation of variants using an ensemble learning framework
Cao, Zhen1,2; Huang, Yanting3; Duan, Ran4; Jin, Peng5; Qin, Zhaohui S.6; Zhang, Shihua1
AbstractUnderstanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.
Keywordcomplex disease disease category functional annotation non-coding variant ensemble learning
Indexed BySCI
Funding ProjectNational Key R&D Program of China[2019YFA0709501] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDPB17] ; Key-Area Research and Development of Guangdong Province[2020B1111190001] ; National Ten Thousand Talent Program for Young Top-notch Talents ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008] ; National Natural Science Foundation of China[61621003]
WOS Research AreaBiochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Mathematical & Computational Biology
WOS IDWOS:000763000800083
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Document Type期刊论文
Corresponding AuthorQin, Zhaohui S.; Zhang, Shihua
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Alibaba Hlth Informat Technol Ltd, Beijing, Peoples R China
3.Emory Univ, Dept Comp Sci, Atlanta, GA 30322 USA
4.Yunnan Univ, Dept Software Engn, Kunming, Yunnan, Peoples R China
5.Emory Univ, Dept Human Genet, Sch Med, Atlanta, GA 30322 USA
6.Emory Univ, Dept Biostat & Bioinformat, Atlanta, GA 30322 USA
Recommended Citation
GB/T 7714
Cao, Zhen,Huang, Yanting,Duan, Ran,et al. Disease category-specific annotation of variants using an ensemble learning framework[J]. BRIEFINGS IN BIOINFORMATICS,2022,23(1):15.
APA Cao, Zhen,Huang, Yanting,Duan, Ran,Jin, Peng,Qin, Zhaohui S.,&Zhang, Shihua.(2022).Disease category-specific annotation of variants using an ensemble learning framework.BRIEFINGS IN BIOINFORMATICS,23(1),15.
MLA Cao, Zhen,et al."Disease category-specific annotation of variants using an ensemble learning framework".BRIEFINGS IN BIOINFORMATICS 23.1(2022):15.
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