期刊
BMC BIOINFORMATICS
卷 22, 期 SUPPL 10, 页码 -出版社
BMC
DOI: 10.1186/s12859-021-04404-0
关键词
Subcellular prediction; Protein sequence; Evolution information; Deep learning; Multiple label classification
类别
资金
- National Key R&D Program of China [2020YFA0908400, KQTD20200820113106007]
- National Natural Science Foundation of China [62072107]
- Natural Science Foundation of Fujian Province of China [2020J01610]
This study introduces a deep learning-based method that utilizes both amino acid composition sequences and evolution matrices of proteins, significantly improving the accuracy of predicting protein subcellular locations.
Background Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations. Results Our method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848. Conclusion The experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.
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