Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data
Background Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a grow...
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| Veröffentlicht in: | BMC bioinformatics Jg. 20; H. 1; S. 709 - 17 |
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BioMed Central
16.12.2019
Springer Nature B.V BMC |
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| Abstract | Background
Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.
Results
We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.
Conclusions
Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease. |
|---|---|
| AbstractList | Background
Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.
Results
We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.
Conclusions
Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease. Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.BACKGROUNDLate-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.RESULTSWe conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.CONCLUSIONSMachine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease. Abstract Background Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. Results We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. Conclusions Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease. Background Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. Results We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. Conclusions Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease. Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease. |
| ArticleNumber | 709 |
| Author | Taméz Peña, José Gerardo De Velasco Oriol, Javier Estrada, Karol Vallejo, Edgar E. Disease Neuroimaging Initiative, The Alzheimer’s |
| Author_xml | – sequence: 1 givenname: Javier orcidid: 0000-0002-7556-245X surname: De Velasco Oriol fullname: De Velasco Oriol, Javier email: javierdevelascooriol@gmail.com organization: Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey – sequence: 2 givenname: Edgar E. surname: Vallejo fullname: Vallejo, Edgar E. organization: Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey – sequence: 3 givenname: Karol surname: Estrada fullname: Estrada, Karol organization: Graduate Professional Studies, Brandeis University – sequence: 4 givenname: José Gerardo surname: Taméz Peña fullname: Taméz Peña, José Gerardo organization: Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey – sequence: 5 givenname: The Alzheimer’s surname: Disease Neuroimaging Initiative fullname: Disease Neuroimaging Initiative, The Alzheimer’s organization: Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey |
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| Cites_doi | 10.1086/519795 10.1016/j.neuroscience.2016.03.051 10.1038/s41598-018-37769-z 10.1001/jama.2010.574 10.1016/j.jalz.2015.05.009 10.1016/j.jalz.2010.03.013 10.1016/j.jocn.2017.06.074 10.1016/S1474-4422(18)30123-6 10.1371/journal.pmed.1002270 10.1038/nature19323 10.1007/s11910-017-0787-1 10.1117/1.JMI.1.3.031005 10.1016/j.ajhg.2013.05.002 10.1002/0471142905.hg0119s68 10.1016/j.artmed.2017.09.005 10.3389/fnagi.2017.00077 10.1016/j.arcmed.2012.11.003 10.1038/mp.2016.18 10.3389/fgene.2019.00267 10.2174/1567205015666180202095616 10.1038/s41598-018-36429-6 10.1016/j.neurobiolaging.2017.05.007 10.1117/12.2008100 10.3174/ajnr.A1809 10.1016/S0140-6736(10)61349-9 10.3389/fnagi.2018.00340 10.3390/ijms20010081 10.1109/IJCNN.2018.8489048 10.1038/ng.2802 10.1016/j.cell.2017.05.038 |
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| Keywords | Benchmarking Genome-wide association studies Alzheimer’s disease Machine learning |
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| References | Evan A. Boyle (3158_CR34) 2017; 169 3158_CR12 Clive Ballard (3158_CR3) 2011; 377 3158_CR32 3158_CR30 Zhi Wei (3158_CR28) 2013; 92 Beatriz López (3158_CR27) 2018; 85 Ana Luisa Sosa-Ortiz (3158_CR1) 2012; 43 J-C Lambert (3158_CR23) 2013; 45 A Lacour (3158_CR33) 2016; 22 Stephen Turner (3158_CR22) 2011; 68 K.B. Walhovd (3158_CR14) 2010; 31 Antonio Martinez-Torteya (3158_CR13) 2014; 1 Shaun Purcell (3158_CR19) 2007; 81 M. Panpalli Ates (3158_CR9) 2016; 325 3158_CR18 Wen Shao (3158_CR6) 2017; 45 3158_CR17 3158_CR15 Jin Li (3158_CR5) 2017; 57 3158_CR24 Hélène-Marie Lanoiselée (3158_CR2) 2017; 14 Carole Dufouil (3158_CR25) 2018; 17 Andrew J. Saykin (3158_CR10) 2010; 6 3158_CR21 Sudha Seshadri (3158_CR7) 2010; 303 3158_CR20 J Sevigny (3158_CR4) 2016; 537 Cody Wolfe (3158_CR31) 2018; 20 3158_CR8 Andrew J. Saykin (3158_CR16) 2015; 11 Antonio Martinez-Torteya (3158_CR11) 2018; 15 3158_CR29 3158_CR26 |
| References_xml | – volume: 81 start-page: 559 issue: 3 year: 2007 ident: 3158_CR19 publication-title: The American Journal of Human Genetics doi: 10.1086/519795 – volume: 325 start-page: 124 year: 2016 ident: 3158_CR9 publication-title: Neuroscience doi: 10.1016/j.neuroscience.2016.03.051 – ident: 3158_CR18 – ident: 3158_CR15 doi: 10.1038/s41598-018-37769-z – volume: 303 start-page: 1832 issue: 18 year: 2010 ident: 3158_CR7 publication-title: JAMA doi: 10.1001/jama.2010.574 – volume: 11 start-page: 792 issue: 7 year: 2015 ident: 3158_CR16 publication-title: Alzheimer's & Dementia doi: 10.1016/j.jalz.2015.05.009 – volume: 6 start-page: 265 issue: 3 year: 2010 ident: 3158_CR10 publication-title: Alzheimer's & Dementia doi: 10.1016/j.jalz.2010.03.013 – volume: 45 start-page: 1 year: 2017 ident: 3158_CR6 publication-title: Journal of Clinical Neuroscience doi: 10.1016/j.jocn.2017.06.074 – volume: 17 start-page: 388 issue: 5 year: 2018 ident: 3158_CR25 publication-title: The Lancet Neurology doi: 10.1016/S1474-4422(18)30123-6 – volume: 14 start-page: e1002270 issue: 3 year: 2017 ident: 3158_CR2 publication-title: PLOS Medicine doi: 10.1371/journal.pmed.1002270 – volume: 537 start-page: 50 year: 2016 ident: 3158_CR4 publication-title: Nature doi: 10.1038/nature19323 – ident: 3158_CR8 doi: 10.1007/s11910-017-0787-1 – volume: 1 start-page: 031005 issue: 3 year: 2014 ident: 3158_CR13 publication-title: Journal of Medical Imaging doi: 10.1117/1.JMI.1.3.031005 – ident: 3158_CR20 – volume: 92 start-page: 1008 issue: 6 year: 2013 ident: 3158_CR28 publication-title: The American Journal of Human Genetics doi: 10.1016/j.ajhg.2013.05.002 – volume: 68 start-page: 1.19.1-1.19.18 issue: 1 year: 2011 ident: 3158_CR22 publication-title: Current Protocols in Human Genetics doi: 10.1002/0471142905.hg0119s68 – volume: 85 start-page: 43 year: 2018 ident: 3158_CR27 publication-title: Artificial Intelligence in Medicine doi: 10.1016/j.artmed.2017.09.005 – ident: 3158_CR26 doi: 10.3389/fnagi.2017.00077 – volume: 43 start-page: 600 issue: 8 year: 2012 ident: 3158_CR1 publication-title: Archives of Medical Research doi: 10.1016/j.arcmed.2012.11.003 – ident: 3158_CR17 – volume: 22 start-page: 153 year: 2016 ident: 3158_CR33 publication-title: Mole Psych doi: 10.1038/mp.2016.18 – ident: 3158_CR30 doi: 10.3389/fgene.2019.00267 – volume: 15 start-page: 751 issue: 8 year: 2018 ident: 3158_CR11 publication-title: Current Alzheimer Research doi: 10.2174/1567205015666180202095616 – ident: 3158_CR32 doi: 10.1038/s41598-018-36429-6 – volume: 57 start-page: 247.e1-247.e8 year: 2017 ident: 3158_CR5 publication-title: Neurobiology of Aging doi: 10.1016/j.neurobiolaging.2017.05.007 – ident: 3158_CR12 doi: 10.1117/12.2008100 – volume: 31 start-page: 347 issue: 2 year: 2010 ident: 3158_CR14 publication-title: American Journal of Neuroradiology doi: 10.3174/ajnr.A1809 – volume: 377 start-page: 1019 issue: 9770 year: 2011 ident: 3158_CR3 publication-title: The Lancet doi: 10.1016/S0140-6736(10)61349-9 – ident: 3158_CR24 doi: 10.3389/fnagi.2018.00340 – volume: 20 start-page: 81 issue: 1 year: 2018 ident: 3158_CR31 publication-title: International Journal of Molecular Sciences doi: 10.3390/ijms20010081 – ident: 3158_CR21 – ident: 3158_CR29 doi: 10.1109/IJCNN.2018.8489048 – volume: 45 start-page: 1452 year: 2013 ident: 3158_CR23 publication-title: Nat Genet doi: 10.1038/ng.2802 – volume: 169 start-page: 1177 issue: 7 year: 2017 ident: 3158_CR34 publication-title: Cell doi: 10.1016/j.cell.2017.05.038 |
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Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on... Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive... Background Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on... Abstract Background Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to... |
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| SubjectTerms | Accuracy Age Age of Onset Aged Algorithms Alzheimer Disease - genetics Alzheimer's disease Artificial intelligence Benchmarking Bioinformatics Biomarkers Biomedical and Life Sciences Classification Cognitive ability Cohort Studies Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Dementia Dementia disorders Female Genetic diversity Genetic markers Genome-wide association studies Genomics Humans Learning algorithms Life Sciences Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Male Medical imaging Methods Microarrays Neurodegenerative diseases Neuroimaging Neuroimaging - methods NMR Nuclear magnetic resonance Predictions Research Article ROC Curve |
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| Title | Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data |
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