ReviewEmerging topics in life sciences2021
Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.
Review in Emerging topics in life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.Metabolic brain disease · 2024Pooled it
- A new AI assisted approach aligns data standards and accelerates interoperability in biomedical research.NPJ digital medicine · 2026Article
- Olfactory radiomics signatures link pTau217 to cognitive impairment in probable Alzheimer's disease: a multi-cohort machine learning study.Alzheimer's research & therapy · 2026Article
- Decoding the Metabolic Signatures of Neurodegeneration Diseases: Advances in Mass Spectrometry-Based Metabolomics.Metabolites · 2026Review
- Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.Brain and behavior · 2026Article
- Nanobody Therapeutics in Alzheimer's Disease: From Molecular Mechanisms to Translational Approaches.Antibodies (Basel, Switzerland) · 2025Review
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Recent Advances in Antibody Therapy for Alzheimer's Disease: Focus on Bispecific Antibodies.International journal of molecular sciences · 2025Review
- Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer's Disease.International journal of molecular sciences · 2025Review
- IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.Bioinformatics (Oxford, England) · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Cognitive performance classification of older patients using machine learning and electronic medical records.Scientific reports · 2025Article
- Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer's disease research.Frontiers in aging neuroscience · 2025Review
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.Scientific reports · 2024Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease.PLoS biology · 2024Article
- Empirically derived psychosocial-behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS-HD: Associations with AD biomarkers and cognitive outcomes.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders.Microbiome · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
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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.