期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 124, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2020.103933
关键词
Alzheimer's disease; Variants; Machine learning; Histone; Dinucleotide
类别
资金
- Department of Biotechnology (DBT), India
- Department of Biotechnology, Government of India [BT/PR16710/BID/7/680/2016]
Introduction: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments. Objective: The primary purpose of this study is to develop a classification model using machine teaming for predicting the deleterious effect of variants with respect to Al). Methods: We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants. Results: Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range. of 54%-62%. Conclusion: Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/ . VEPAD can be used to predict the effect of new variants associated with AD.
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