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

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Author(s)
Kaleel, Manaz 
Zheng, Yandan 
Chen, Jialiang 
Feng, Xuanming 
Simpson, Jeremy C. 
Pollastri, Gianluca 
Mooney, Catherine 
Uri
http://hdl.handle.net/10197/12182
Date Issued
June 2020
Date Available
19T16:55:36Z May 2021
Abstract
Motivation: 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. Results: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. Contact: catherine.mooney@ucd.ie
Sponsorship
Irish Research Council
Type of Material
Journal Article
Publisher
Oxford University Press
Journal
Bioinformatics
Volume
36
Issue
11
Start Page
3343
End Page
3349
Copyright (Published Version)
2020 the Authors
Keywords
  • Proteins

  • Computational biology...

  • Algorithms

  • Secretory pathway

  • Machine learning

  • Neural networks

DOI
10.1093/bioinformatics/btaa156
Language
English
Status of Item
Peer reviewed
ISSN
1367-4803
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Computer Science Research Collection
Scopus© citations
13
Acquisition Date
Feb 5, 2023
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414
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