Identification of blood-based transcriptomics biomarkers for Alzheimer's disease using statistical and machine learning classifier
Alzheimer's disease (AD) is a neurodegenerative disorder that can be characterised by the gradual progression of memory loss, impairment of cognitive function, and progressive disability. This study aims to find the potential transcriptomics biomarkers that elucidate AD patients in Malaysia. Th...
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| Published in | Informatics in medicine unlocked Vol. 33; p. 101083 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
2022
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-9148 2352-9148 |
| DOI | 10.1016/j.imu.2022.101083 |
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| Abstract | Alzheimer's disease (AD) is a neurodegenerative disorder that can be characterised by the gradual progression of memory loss, impairment of cognitive function, and progressive disability. This study aims to find the potential transcriptomics biomarkers that elucidate AD patients in Malaysia. The sample involves 92 AD patients and 92 non-AD subjects with 22,254 genes. Boruta's feature selection, a method to reduce the dimensionality of the transcriptomics dataset selected 68 genes. The classification performance of four statistical classifiers and three machine learning (ML) classifiers was evaluated based on sensitivity, precision, accuracy, and F-measure. The F-measure statistic (test set) for elastic net LR (mean = 0.9, sd = 0.05) and random forest (mean = 0.79, sd = 0.06) was found to be the highest as compared to other ML classifiers while naïve Bayes has the lowest F-measure (mean = 0.74, sd = 0.07). The elastic net logistic regression results showed there were 16 (4 novel biomarkers, 7 upregulated biomarkers, and 5 downregulated biomarkers) potential biomarkers for AD patients in Malaysia. The elastic net logistic regression model with 16 transcript genes has 81.59% accuracy and 85.19% sensitivity. The F-measure statistic for this model was 0.8159. |
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| AbstractList | Alzheimer's disease (AD) is a neurodegenerative disorder that can be characterised by the gradual progression of memory loss, impairment of cognitive function, and progressive disability. This study aims to find the potential transcriptomics biomarkers that elucidate AD patients in Malaysia. The sample involves 92 AD patients and 92 non-AD subjects with 22,254 genes. Boruta's feature selection, a method to reduce the dimensionality of the transcriptomics dataset selected 68 genes. The classification performance of four statistical classifiers and three machine learning (ML) classifiers was evaluated based on sensitivity, precision, accuracy, and F-measure. The F-measure statistic (test set) for elastic net LR (mean = 0.9, sd = 0.05) and random forest (mean = 0.79, sd = 0.06) was found to be the highest as compared to other ML classifiers while naïve Bayes has the lowest F-measure (mean = 0.74, sd = 0.07). The elastic net logistic regression results showed there were 16 (4 novel biomarkers, 7 upregulated biomarkers, and 5 downregulated biomarkers) potential biomarkers for AD patients in Malaysia. The elastic net logistic regression model with 16 transcript genes has 81.59% accuracy and 85.19% sensitivity. The F-measure statistic for this model was 0.8159. |
| ArticleNumber | 101083 |
| Author | Wah, Yap Bee Abdullah, Mohammad Nasir Zakaria, Yuslina Shaadan, Norshahida Abdul Majeed, Abu Bakar |
| Author_xml | – sequence: 1 givenname: Mohammad Nasir orcidid: 0000-0002-3134-4645 surname: Abdullah fullname: Abdullah, Mohammad Nasir email: nasir916@uitm.edu.my organization: Department of Statistics, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Cawangan Perak, Kampus Tapah, 35400, Tapah, Perak, Malaysia – sequence: 2 givenname: Yap Bee surname: Wah fullname: Wah, Yap Bee email: bee.wah@unitar.my organization: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia – sequence: 3 givenname: Abu Bakar surname: Abdul Majeed fullname: Abdul Majeed, Abu Bakar email: abubakar@uitm.edu.my organization: Brain Research Laboratory, Faculty of Pharmacy, Universiti Teknologi MARA, Cawangan Selangor, Kampus Puncak Alam, 42300 Puncak Alam, Selangor, Malaysia – sequence: 4 givenname: Yuslina surname: Zakaria fullname: Zakaria, Yuslina email: yuslina@uitm.edu.my organization: Faculty of Pharmacy, Universiti Teknologi MARA, Cawangan Selangor, Kampus Puncak Alam, 42300, Puncak Alam, Selangor, Malaysia – sequence: 5 givenname: Norshahida surname: Shaadan fullname: Shaadan, Norshahida email: norshahida588@uitm.edu.my organization: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia |
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| Keywords | Biomarkers Boruta's algorithms Transcriptomics Alzheimer's disease Machine learning |
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