تقرير
Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention
| العنوان: | Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention |
|---|---|
| المؤلفون: | Wadhawan, Kahini, Das, Payel, Han, Barbara A., Fischhoff, Ilya R., Castellanos, Adrian C., Varsani, Arvind, Varshney, Kush R. |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning |
| الوصف: | Current methods for viral discovery target evolutionarily conserved proteins that accurately identify virus families but remain unable to distinguish the zoonotic potential of newly discovered viruses. Here, we apply an attention-enhanced long-short-term memory (LSTM) deep neural net classifier to a highly conserved viral protein target to predict zoonotic potential across betacoronaviruses. The classifier performs with a 94% accuracy. Analysis and visualization of attention at the sequence and structure-level features indicate possible association between important protein-protein interactions governing viral replication in zoonotic betacoronaviruses and zoonotic transmission. Comment: 11 pages, 8 figures, 1 table, accepted at ICLR 2021 workshop Machine learning for preventing and combating pandemics |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2108.08077 |
| رقم الأكسشن: | edsarx.2108.08077 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |