research-article

A Deeper Understanding of Modular DNN in Predicting Ageing-Related Disease

Authors:

Danyating ShenAuthors Info & Claims

DMIP '21: Proceedings of the 2021 4th International Conference on Digital Medicine and Image Processing

Pages 45 - 53

https://doi.org/10.1145/3506651.3506659

Published: 21 February 2022 Publication History

Abstract

Ageing is a significant process happening in all humans and close related to health and lifetime. However, the mechanism of ageing is poorly understood. Getting to know about which specific genes control ageing-related diseases can be a great help of this mechanism. This paper focuses on using one of the most advanced machine learning methods nowadays to predict ageing related disease with large amount of genes. This paper finds a deeper relation behind the different datasets and encoders of modular DNN raised by Fabio Fabris’ group. With a deeper understanding of modular DNN, this paper is able to find a model with AUC value equal to 0.9732, which has a 10.65% improvement compared with former paper. With the results and final model of this paper, this paper can help scientists identify high-possible ageing-related genes with higher accuracy.

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Fabio Fabris, Daniel Palmer, Khalid M Salama, João Pedro de Magalhães, Alex A Freitas, Using deep learning to associate human genes with age-related diseases, Bioinformatics, Volume 36, Issue 7, 1 April 2020, Pages 2202–2208, https://doi.org/10.1093/bioinformatics/btz887

Cited By

Khalid MAli FAlghamdi WAlzahrani AAlsini RAlzahrani A(2024)An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transformJournal of Biomolecular Structure and Dynamics10.1080/07391102.2024.2329777(1-9)Online publication date: 18-Mar-2024
https://doi.org/10.1080/07391102.2024.2329777

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cover image ACM Other conferences

DMIP '21: Proceedings of the 2021 4th International Conference on Digital Medicine and Image Processing

November 2021

87 pages

ISBN:9781450386487

DOI:10.1145/3506651

Copyright © 2021 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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Publication History

Published: 21 February 2022

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DMIP '21

DMIP '21: 2021 4th International Conference on Digital Medicine and Image Processing

November 12 - 15, 2021

Kyoto, Japan

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Cited By

Khalid MAli FAlghamdi WAlzahrani AAlsini RAlzahrani A(2024)An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transformJournal of Biomolecular Structure and Dynamics10.1080/07391102.2024.2329777(1-9)Online publication date: 18-Mar-2024
https://doi.org/10.1080/07391102.2024.2329777

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