Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier | Zendy

Yushuang Liu | Zendy; Shuping Jin | Zendy; Hongli Gao | Zendy; Xue Wang | Zendy; Congjing Wang | Zendy; Weifeng Zhou | Zendy; Bin Yu | Zendy

Open Access

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

Author(s) -

Yushuang Liu,

Shuping Jin,

Hongli Gao,

Xue Wang,

Congjing Wang,

Weifeng Zhou,

Bin Yu

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab811

Subject(s) - dimensionality reduction , classifier (uml) , artificial intelligence , computer science , pattern recognition (psychology) , fusion , machine learning , philosophy , linguistics

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).

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