research-article

Enhanced Characterization Performance of Propionylation PTM utilizing multiple feature fusion

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Boshir AhmedAuthors Info & Claims

ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements

Pages 1 - 5

https://doi.org/10.1145/3542954.3542955

Published: 11 August 2022 Publication History

Abstract

Propionylation post-transnational modification (PTM) is a relatively new and salient modification occurs in the side chain of lysine amino acids under different conditions in both eukaryotes and prokaryotes living cells. It has significant roles in the metabolic processes, regulation of bio-molecules, biological activities including various adverse health problems. Characterization of propionylation PTMs in laboratory using mass-spectrometer requires expertise involvement, moreover, the process is sluggish and cost ineffective. Development of computational models can replace the laboratory methods expanding its global reach to any level of users. We developed an efficient model combining assorted features like physicochemical, amino acid frequency and evolutionary based features by feeding into different classifiers. We managed to get accuracy, sensitivity, specificity and MCC of 95.12 ± 0.8%, 92.41 ± 1.7%, 97.82 ± 1.2% and 90.42 ± 1.6% respectively which outperformed the previously developed state-of-the-art models.

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Digital Library

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Cited By

Arafat MAhmad MShovan SHaq TIslam NMahmud MKaiser M(2024)Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based InformationCognitive Computation10.1007/s12559-024-10268-216:3(1300-1320)Online publication date: 2-May-2024
https://doi.org/10.1007/s12559-024-10268-2

Index Terms

Enhanced Characterization Performance of Propionylation PTM utilizing multiple feature fusion
1. Applied computing
  1. Life and medical sciences

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ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements

March 2022

543 pages

ISBN:9781450397346

DOI:10.1145/3542954

Copyright © 2022 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 ACM 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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Published: 11 August 2022

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ICCA 2022

ICCA 2022: 2nd International Conference on Computing Advancements

March 10 - 12, 2022

Dhaka, Bangladesh

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Cited By

Arafat MAhmad MShovan SHaq TIslam NMahmud MKaiser M(2024)Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based InformationCognitive Computation10.1007/s12559-024-10268-216:3(1300-1320)Online publication date: 2-May-2024
https://doi.org/10.1007/s12559-024-10268-2

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