Title | ||
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Predicting the Subcellular Localization of Multi-site Protein Based on Fusion Feature and Multi-label Deep Forest Model |
Abstract | ||
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Protein is the basis of life activities and plays an irreplaceable role. Proteins usually have a specific biochemical environment that is closely related to protein function, so understanding the subcellular localization information of proteins can provide powerful help for the research of drugs for the treatment of diseases. The prediction of protein subcellular localization is a very basic and significant in bioinformatics. In this paper, we propose a method for predicting the subcellular localization of multi-site bacterial proteins. Based on position-specific scoring matrix (PSSM) containing evolutionary information, we first extract two new features to represent proteins, of which one is from functional amino acid analysis and another is from Jensen-Shannon (JS) divergence analysis. After combining two features, we obtain a new fusion feature which contains more information and input it into the multi-label deep forest (MLDF) model for subcellular localization prediction of multi-site proteins. Experimental results on Gram-positive data set show the effectiveness of the proposed method. |
Year | DOI | Venue |
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2022 | 10.1007/978-3-031-13829-4_28 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2022, PT II |
Keywords | DocType | Volume |
Multi-site protein subcellular localization, PSSM, JS divergence, Multi-label deep forest | Conference | 13394 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hongri Yang | 1 | 0 | 0.34 |
Qingfang Meng | 2 | 0 | 0.34 |
Yuehui Chen | 3 | 0 | 0.34 |
Lianxin Zhong | 4 | 0 | 0.34 |