ReviewJournal of personalized medicine2021
Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Computational approach for the evaluation of sesquiterpene lactone as a modulator of cannabinoid receptor type 2 for neurodegenerative disease prophylactics.Molecular diversity · 2026Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Review
- Paradigm shift required for translational research on the brain.Experimental & molecular medicine · 2024Review
- Automated pipeline for denoising, missing data processing, and feature extraction for signals acquired via wearable devices in multiple sclerosis and amyotrophic lateral sclerosis applications.Frontiers in digital health · 2024Article
- Advances of Genome Editing with CRISPR/Cas9 in Neurodegeneration: The Right Path towards Therapy.Biomedicines · 2023Review
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives.Ageing research reviews · 2023Review
- Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
- Role of non‑coding RNAs as biomarkers and the application of omics technologies in Alzheimer's disease (Review).International journal of molecular medicine · 2023Review
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD.Cells · 2022Review
- A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks.Life (Basel, Switzerland) · 2022Article
- A Hybrid Machine Learning and Network Analysis Approach Reveals Two Parkinson's Disease Subtypes from 115 RNA-Seq Post-Mortem Brain Samples.International journal of molecular sciences · 2022Article
- Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases through the integration of all sources of biomedical data.
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What OpenQuestion holds
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.