SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks | Zendy

Manaz Kaleel | Zendy; Yandan Zheng | Zendy; Jialiang Chen | Zendy; Xuanming Feng | Zendy; Jeremy C. Simpson | Zendy; Gianluca Pollastri | Zendy; Catherine Mooney | Zendy

Open Access

SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks

Author(s) -

Manaz Kaleel,

Yandan Zheng,

Jialiang Chen,

Xuanming Feng,

Jeremy C. Simpson,

Gianluca Pollastri,

Catherine Mooney

Publication year - 2020

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/btaa156

Subject(s) - endomembrane system , subcellular localization , computer science , convolutional neural network , artificial neural network , artificial intelligence , protein subcellular localization prediction , machine learning , biology , microbiology and biotechnology , biochemistry , golgi apparatus , endoplasmic reticulum , cytoplasm , gene

The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins.

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