Prediction of disease-associated functional variants in non-coding regions through a comprehensive analysis by integrating datasets and features
| Main Authors: | Yu Lu, Yiming Wu, Yuan Liu, Yizhou Li, Runyu Jing, Menglong Li |
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| Format: | info Lainnya Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4460825 |
Daftar Isi:
- Abstract One of the greatest challenges in human genetics is deciphering the link between functional variants in non-coding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single nucleotide variants (SNVs) for neutral SNVs in non-coding regions. In this study, we integrated well-established features and commonly used datasets, and merged them into large-scale datasets based on a random forest model, which yielded promising performance. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional non-coding SNVs.