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Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis

Authors:

Trilce Estrada,

Hector Carrillo-Cabada,

Asghar M. Razavi,

Michel A. Cuendet,

Harel Weinstein,

Michela TauferAuthors Info & Claims

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 315 - 324

https://doi.org/10.1145/3233547.3233607

Published: 15 August 2018 Publication History

Abstract

The function of a protein depends on its three-dimensional structure. Current approaches based on homology for predicting a given protein's function do not work well at scale. In this work, we propose a representation of proteins that explicitly encodes secondary and tertiary structure into fix-sized images. In addition, we present a neural network architecture that exploits our data representation to perform protein function prediction. We validate the effectiveness of our encoding method and the strength of our neural network architecture through a 5-fold cross validation over roughly 63 thousand images, achieving an accuracy of 80% across 8 distinct classes. Our novel approach of encoding and classifying proteins is suitable for real-time processing, leading to high-throughput analysis.

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Cited By

Doudali TGavrilovska A(2022)Cronus: Computer Vision-based Machine Intelligent Hybrid Memory ManagementProceedings of the 2022 International Symposium on Memory Systems10.1145/3565053.3565063(1-11)Online publication date: 3-Oct-2022
https://dl.acm.org/doi/10.1145/3565053.3565063
Lemieux GPaquet EViktor HMichalowski W(2022)Geometric Deep Learning for Protein–Protein Interaction PredictionsIEEE Access10.1109/ACCESS.2022.320154310(90045-90055)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3201543
Carrillo-Cabada HBenson JRazavi AMulligan BCuendet MWeinstein HTaufer MEstrada T(2021)A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational ChangesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2019.294529118:4(1336-1349)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TCBB.2019.2945291
Show More Cited By

Index Terms

Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Molecular structural biology
2. Computing methodologies

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Published In

cover image ACM Conferences

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2018

727 pages

ISBN:9781450357944

DOI:10.1145/3233547

General Chairs:
Amarda Shehu
George Mason University, USA
,
Cathy Wu
University of Delaware, USA
,
Program Chairs:
Christina Boucher
University of Florida, USA
,
Jing Li
Case Western Reserve University, USA
,
Hongfang Liu
Mayo Clinic, USA
,
Mihai Pop
University of Maryland, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

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Published: 15 August 2018

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National Science Foundation

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BCB '18

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SIGBio

BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 29 - September 1, 2018

DC, Washington, USA

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BCB '18 Paper Acceptance Rate 46 of 148 submissions, 31%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Doudali TGavrilovska A(2022)Cronus: Computer Vision-based Machine Intelligent Hybrid Memory ManagementProceedings of the 2022 International Symposium on Memory Systems10.1145/3565053.3565063(1-11)Online publication date: 3-Oct-2022
https://dl.acm.org/doi/10.1145/3565053.3565063
Lemieux GPaquet EViktor HMichalowski W(2022)Geometric Deep Learning for Protein–Protein Interaction PredictionsIEEE Access10.1109/ACCESS.2022.320154310(90045-90055)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3201543
Carrillo-Cabada HBenson JRazavi AMulligan BCuendet MWeinstein HTaufer MEstrada T(2021)A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational ChangesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2019.294529118:4(1336-1349)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TCBB.2019.2945291
Taufer MEstrada TJohnston T(2020) A survey of algorithms for transforming molecular dynamics data into metadata for in situ analytics based on machine learning methods Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences10.1098/rsta.2019.0063378:2166(20190063)Online publication date: 20-Jan-2020
https://doi.org/10.1098/rsta.2019.0063

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