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MILAMP : multiple instance prediction of amyloid proteins
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Munir, Farzeen, Gull, Sadaf, Asif, Amina and Minhas, Fayyaz ul Amir Afsar (2019) MILAMP : multiple instance prediction of amyloid proteins. IEEE/ACM Transactions on Computational Biology and Bioinformatics . p. 1. doi:10.1109/TCBB.2019.2936846 ISSN 1545-5963.
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WRAP-MILAMP-multiple-instance-prediction-amyloid-proteins-Minhas-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1438Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TCBB.2019.2936846
Abstract
Amyloid proteins are implicated in several diseases such as Parkinson’s, Alzheimer’s, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QD Chemistry Q Science > QP Physiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Amyloid -- Research, Machine learning, Proteins -- Metabolism -- Disorders , | |||||||||
Journal or Publication Title: | IEEE/ACM Transactions on Computational Biology and Bioinformatics | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 1545-5963 | |||||||||
Official Date: | 22 August 2019 | |||||||||
Dates: |
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Page Range: | p. 1 | |||||||||
DOI: | 10.1109/TCBB.2019.2936846 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 4 November 2019 | |||||||||
Date of first compliant Open Access: | 5 November 2019 | |||||||||
RIOXX Funder/Project Grant: |
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