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Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network
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DOI：10.1007/s12539-022-00529-9.png)
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Abstract
En 中文
Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc. [GRAPHICS] .
Keywords:
Protein subcellular localization
Deep learning
Graph convolutional network
Multi-head attention
Journal
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3.9
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947
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1.5K