Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarke...

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Veröffentlicht in:Bioinformation 2021-02, Vol.17 (2), p.348-355
Hauptverfasser: Madar, Inamul Hasan, Sultan, Ghazala, Tayubi, Iftikhar Aslam, Hasan, Atif Noorul, Pahi, Bandana, Rai, Anjali, Sivanandan, Pravitha Kasu, Loganathan, Tamizhini, Begum, Mahamuda, Rai, Sneha
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container_issue 2
container_start_page 348
container_title Bioinformation
container_volume 17
creator Madar, Inamul Hasan
Sultan, Ghazala
Tayubi, Iftikhar Aslam
Hasan, Atif Noorul
Pahi, Bandana
Rai, Anjali
Sivanandan, Pravitha Kasu
Loganathan, Tamizhini
Begum, Mahamuda
Rai, Sneha
description Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.
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subjects Algorithms
Alzheimer's disease
Annotations
Biomarkers
Classification
Classifiers
Dementia disorders
Gene expression
Genes
Learning algorithms
Machine learning
Neurodegenerative diseases
Older people
Tissues
title Identification of marker genes in Alzheimer's disease using a machine-learning model
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