Browsing by Author "Wang, Ye"

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    Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence
    (Oxford University Press, 2022) Chen, Li; Wang, Ye; Zhao, Fengdi; Biostatistics, School of Public Health
    Motivation: Though genome-wide association studies have identified tens of thousands of variants associated with complex traits and most of them fall within the non-coding regions, they may not be the causal ones. The development of high-throughput functional assays leads to the discovery of experimental validated non-coding functional variants. However, these validated variants are rare due to technical difficulty and financial cost. The small sample size of validated variants makes it less reliable to develop a supervised machine learning model for achieving a whole genome-wide prediction of non-coding causal variants. Results: We will exploit a deep transfer learning model, which is based on convolutional neural network, to improve the prediction for functional non-coding variants (NCVs). To address the challenge of small sample size, the transfer learning model leverages both large-scale generic functional NCVs to improve the learning of low-level features and context-specific functional NCVs to learn high-level features toward the context-specific prediction task. By evaluating the deep transfer learning model on three MPRA datasets and 16 GWAS datasets, we demonstrate that the proposed model outperforms deep learning models without pretraining or retraining. In addition, the deep transfer learning model outperforms 18 existing computational methods in both MPRA and GWAS datasets. Availability and implementation: https://github.com/lichen-lab/TLVar.
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    WEVar: a novel statistical learning framework for predicting noncoding regulatory variants
    (Oxford University Press, 2021) Wang, Ye; Jiang, Yuchao; Yao, Bing; Huang, Kun; Liu, Yunlong; Wang, Yue; Qin, Xiao; Saykin, Andrew J.; Chen, Li; Biostatistics and Health Data Science, School of Medicine
    Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for prioritizing noncoding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first 'context-free' mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict regulatory variants of unknown and diverse context. The second 'context-dependent' mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.