SCLpred-EMS: Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks
|Title:||SCLpred-EMS: Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks||Authors:||Kaleel, Manaz; Zheng, Yandan; Chen, Jialiang; Feng, Xuanming; Simpson, Jeremy C.; Pollastri, Gianluca; Mooney, 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: email@example.com||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:||Proteins; Computational biology; Algorithms; Secretory pathway; Machine learning; Neural 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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