Weighted graphlets and deep neural networks for protein structure classification

Hongyu Guo; Khalique Newaz; Scott Emrich; Tijana Milenkovic; Jun Li

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arXiv · 1910.02594

Weighted graphlets and deep neural networks for protein structure classification

Hongyu Guo·Khalique Newaz·Scott Emrich·Tijana Milenkovic·Jun Li

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Abstract

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.

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BibTeX RIS

Hongyu Guo, Khalique Newaz, Scott Emrich, Tijana Milenkovic, Jun Li. 2019-10-07. Weighted graphlets and deep neural networks for protein structure classification. https://arxiv.org/abs/1910.02594

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