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

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Title: SCLpred-EMS: Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks
Authors: Kaleel, ManazZheng, YandanChen, JialiangFeng, XuanmingSimpson, Jeremy C.Pollastri, GianlucaMooney, Catherine
Permanent link: http://hdl.handle.net/10197/12182
Date: Jun-2020
Online since: 2021-05-19T16:55:36Z
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
Funding Details: 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: ProteinsComputational biologyAlgorithmsSecretory pathwayMachine learningNeural networks
DOI: 10.1093/bioinformatics/btaa156
Language: en
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/
Appears in Collections:Conway Institute Research Collection
Computer Science Research Collection
Biology & Environmental Science Research Collection

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