ArticleScientific reports2018
Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer's Disease and Reconstruction of Relevant Biological Mechanisms.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer's disease created by integrative analysis of multi-omics data.Alzheimer's research & therapy · 2020Pooled it
- Endoplasmic reticulum stress in skeletal muscle dysfunction of type 2 diabetes: mechanisms and therapeutic implications.Frontiers in endocrinology · 2026Review
- Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review.Journal of personalized medicine · 2024Review
- The causal link between cardiometabolic risk factors and gray matter atrophy: An exploratory study.Heliyon · 2023Article
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- Article
- Forecasting individual progression trajectories in Alzheimer's disease.Nature communications · 2023Article
- Monte Carlo cross-validation for a study with binary outcome and limited sample size.BMC medical informatics and decision making · 2022Article
- A multi-expert ensemble system for predicting Alzheimer transition using clinical features.Brain informatics · 2022Article
- Identifying Alzheimer's genes via brain transcriptome mapping.BMC medical genomics · 2022Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors.Frontiers in aging neuroscience · 2022Article
- Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine.Frontiers in molecular medicine · 2022Review
- Classifying mild cognitive impairment and Alzheimer's disease by constructing a 14-gene diagnostic model.American journal of translational research · 2022Article
- Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models.Neurobiology of disease · 2021Review
- CLEP: a hybrid data- and knowledge-driven framework for generating patient representations.Bioinformatics (Oxford, England) · 2021Article
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
- Bayesian Network as a Decision Tool for Predicting ALS Disease.Brain sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's Disease (AD) is among the most frequent neuro-degenerative diseases. Early diagnosis is essential for successful disease management and chance to attenuate symptoms by disease modifying drugs. In the past, a number of cerebrospinal fluid (CSF), plasma and neuro-imaging based biomarkers have been proposed. Still, in current clinical practice, AD diagnosis cannot be made until the patient shows clear signs of cognitive decline, which can partially be attributed to the multi-factorial nature of AD. In this work, we integrated genotype information, neuro-imaging as well as clinical data (including neuro-psychological measures) from ~900 normal and mild cognitively impaired (MCI) individuals and developed a highly accurate machine learning model to predict the time until AD is diagnosed. We performed an in-depth investigation of the relevant baseline characteristics that contributed to the AD risk prediction. More specifically, we used Bayesian Networks to uncover the interplay across biological scales between neuro-psychological assessment scores, single genetic variants, pathways and neuro-imaging related features. Together with information extracted from the literature, this allowed us to partially reconstruct biological mechanisms that could play a role in the conversion of normal/MCI into AD pathology. This in turn may open the door to novel therapeutic options in the future.
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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 OpenQuestion graph.