ArticleJournal of clinical medicine2020
Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross-Platform Validation.Annals of clinical and translational neurology · 2026Article
- A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.Biomedical engineering and computational biology · 2026Review
- Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Analyte Importance Analysis in Machine Learning-Based Detection of Wrong-Blood-in-Tube Errors Using Complete Blood Count Data.Journal of personalized medicine · 2025Article
- Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer's Disease.International journal of molecular sciences · 2025Review
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Innovative approaches to metabolic dysfunction-associated steatohepatitis diagnosis and stratification.Non-coding RNA research · 2025Article
- Exosomal mRNA Signatures as Predictive Biomarkers for Risk and Age of Onset in Alzheimer's Disease.International journal of molecular sciences · 2024Article
- A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.Translational psychiatry · 2024Article
- Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.Frontiers in immunology · 2024Review
- Article
- A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.IBRO neuroscience reports · 2023Article
- Blood biomarker-based classification study for neurodegenerative diseases.Scientific reports · 2023Article
- Use Test of Automated Machine Learning in Cancer Diagnostics.Diagnostics (Basel, Switzerland) · 2023Article
- Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification.Biomedicines · 2023Article
- Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia.Frontiers in bioengineering and biotechnology · 2023Article
- PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.International journal of molecular sciences · 2022Article
- Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.