ArticleJournal of Alzheimer's disease : JAD2020
Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease.
Article in Journal of Alzheimer's disease : JAD, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.International journal of molecular sciences · 2020Trial
- Interpretable neural network for risk stratification and drug target discovery based on PBMC transcriptomes.iScience · 2026Article
- Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion.BioData mining · 2024Article
- Molecular Gene Expression Testing to Identify Alzheimer's Disease with High Accuracy from Fingerstick Blood.Journal of Alzheimer's disease : JAD · 2024Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Research on Voxel-Based Features Detection and Analysis of Alzheimer's Disease Using Random Survey Support Vector Machine.Frontiers in neuroinformatics · 2022Article
- Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.Journal of clinical medicine · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.
objectiveInvestigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.
methodsTwo types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.
resultsThe AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).
conclusionsThis early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.
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