ReviewFrontiers in molecular biosciences2019
Challenges of Integrative Disease Modeling in Alzheimer's Disease.
Review in Frontiers in molecular biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Systems biology of Alzheimer's Disease: a scoping review of key pathways and mechanisms.Molecular neurodegeneration · 2026Article
- Comorbidity sequence, sex, and APOE-genotype forecast Alzheimer's disease diagnosis.Frontiers in medicine · 2026Article
- A Multi-Label Deep Learning Model for Detailed Classification of Alzheimer's Disease.Actas espanolas de psiquiatria · 2025Article
- Article
- A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease.Biomolecules · 2023Article
- Harnessing the potential of machine learning and artificial intelligence for dementia research.Brain informatics · 2023Review
- ADataViewer: exploring semantically harmonized Alzheimer's disease cohort datasets.Alzheimer's research & therapy · 2022Article
- Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia.Medical image analysis · 2022Article
- Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021.The EPMA journal · 2021Article
- Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort.Frontiers in big data · 2021Review
- Analysis of Age-Dependent Alterations in Excitability Properties of CA1 Pyramidal Neurons in an APPPS1 Model of Alzheimer's Disease.Frontiers in aging neuroscience · 2021Article
- Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain.Frontiers in neuroinformatics · 2021Review
- Disease progression modeling of Alzheimer's disease according to education level.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
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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.