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A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects
Open AccessArticle

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning

Laboratory of Pharmacology, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Gnosis Data Analysis PC, Science and Technology Park of Crete, N. Plastira 100, GR-700 13 Vassilika Vouton, Greece
Department of Computer Science, University of Crete, GR-700 13 Vassilika Vouton, Greece
Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas, GR-700 13 Vassilika Vouton, Greece
Institute of Agri-Food and Life Sciences, University Research Centre, Hellenic Mediterranean University, GR-71410 Heraklion, Greece
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(9), 3016;
Received: 3 August 2020 / Revised: 4 September 2020 / Accepted: 14 September 2020 / Published: 18 September 2020
(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)
Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery. View Full-Text
Keywords: Alzheimer’s disease; predictive model; machine learning; blood; classifier Alzheimer’s disease; predictive model; machine learning; blood; classifier
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Karaglani, M.; Gourlia, K.; Tsamardinos, I.; Chatzaki, E. Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning. J. Clin. Med. 2020, 9, 3016.

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