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Liu Y, Jin S, Gao H, Wang X, Wang C, Zhou W, Yu B. Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier. Bioinformatics 2021; 38:1223-1230. [PMID: 34864897 PMCID: PMC8690230 DOI: 10.1093/bioinformatics/btab811] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Multi-label (ML) protein subcellular localization (SCL) is an indispensable way to study protein function. It can locate a certain protein (such as the human transmembrane protein that promotes the invasion of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) or expression product at a specific location in a cell, which can provide a reference for clinical treatment of diseases such as coronavirus disease 2019 (COVID-19). RESULTS The article proposes a novel method named ML-locMLFE. First of all, six feature extraction methods are adopted to obtain protein effective information. These methods include pseudo amino acid composition, encoding based on grouped weight, gene ontology, multi-scale continuous and discontinuous, residue probing transformation and evolutionary distance transformation. In the next part, we utilize the ML information latent semantic index method to avoid the interference of redundant information. In the end, ML learning with feature-induced labeling information enrichment is adopted to predict the ML protein SCL. The Gram-positive bacteria dataset is chosen as a training set, while the Gram-negative bacteria dataset, virus dataset, newPlant dataset and SARS-CoV-2 dataset as the test sets. The overall actual accuracy of the first four datasets are 99.23%, 93.82%, 93.24% and 96.72% by the leave-one-out cross validation. It is worth mentioning that the overall actual accuracy prediction result of our predictor on the SARS-CoV-2 dataset is 72.73%. The results indicate that the ML-locMLFE method has obvious advantages in predicting the SCL of ML protein, which provides new ideas for further research on the SCL of ML protein. AVAILABILITY AND IMPLEMENTATION The source codes and datasets are publicly available at https://github.com/QUST-AIBBDRC/ML-locMLFE/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yushuang Liu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Shuping Jin
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Hongli Gao
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Xue Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Congjing Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Weifeng Zhou
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Bin Yu
- School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China,College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China,To whom correspondence should be addressed.
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