Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Transfer Learning for Classification of Alzheimer's Disease Based on Genome Wide Data

Alatrany, AS, Khan, W, Hussain, AJ, Mustafina, J and Al-Jumeily, D (2023) Transfer Learning for Classification of Alzheimer's Disease Based on Genome Wide Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20 (5). pp. 2700-2711. ISSN 1545-5963

Transfer_Learning_for_Classification_of_Alzheimers_Disease_Based_on_Genome_Wide_Data.pdf - Accepted Version

Download (1MB) | Preview


Alzheimer's disease (AD) is a type of brain disorder that is regarded as a degenerative disease because the corresponding symptoms aggravate with the time progression. Single nucleotide polymorphisms (SNPs) have been identified as relevant biomarkers for this condition. This study aims to identify SNPs biomarkers associated with the AD in order to perform a reliable classification of AD. In contrast to existing related works, we utilize deep transfer learning with varying experimental analysis for reliable classification of AD. For this purpose, the convolutional neural networks (CNN) are firstly trained over the genome-wide association studies (GWAS) dataset requested from the AD neuroimaging initiative. We then employ the deep transfer learning for further training of our CNN (as base model) over a different AD GWAS dataset, to extract the final set of features. The extracted features are then fed into Support Vector Machine for classification of AD. Detailed experiments are performed using multiple datasets and varying experimental configurations. The statistical outcomes indicate an accuracy of 89% which is a significant improvement when benchmarked with existing related works.

Item Type: Article
Additional Information: © 2023 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.
Uncontrolled Keywords: 01 Mathematical Sciences; 06 Biological Sciences; 08 Information and Computing Sciences; Bioinformatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Q Science > QH Natural history > QH301 Biology
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor: A Symplectic
Date Deposited: 20 Jun 2023 12:08
Last Modified: 06 Nov 2023 17:01
DOI or ID number: 10.1109/TCBB.2022.3233869
URI: https://researchonline.ljmu.ac.uk/id/eprint/19960
View Item View Item