Weighted graphlets and deep neural networks for protein structure classification
This work addresses protein function prediction for computational biology, offering a novel method that is incremental but with strong specific gains.
The authors tackled protein structure classification by developing a weighted graphlet measure for networks and a deep neural network classifier, achieving dramatic improvements over existing methods and nearly halving the error compared to the state-of-the-art on 36 real datasets.
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.