تقرير
Weighted graphlets and deep neural networks for protein structure classification
| العنوان: | Weighted graphlets and deep neural networks for protein structure classification |
|---|---|
| المؤلفون: | Guo, Hongyu, Newaz, Khalique, Emrich, Scott, Milenkovic, Tijana, Li, Jun |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Quantitative Biology Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Quantitative Biology - Biomolecules |
| الوصف: | As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks. Further, we develop a deep neural network (DNN) composed of both convolutional and recurrent layers to use this measure for classification. Put together, our approach shows dramatic improvements in performance over existing graphlet-based approaches on 36 real datasets. Even comparing with the state-of-the-art approach, it almost halves the classification error. In addition to protein structure networks, our weighted-graphlet measure and DNN classifier can potentially be applied to classification of other weighted networks in computational biology as well as in other domains. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1910.02594Test |
| رقم الانضمام: | edsarx.1910.02594 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |