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.
Highlights
We reviewed how Artificial Intelligence (AI) can be applied to biomedical big data for Neurodegenerative Diseases (ND)
Creating straightforward and interpretable Deep Learning (DL) models is a challenge for AI research in the healthcare field and several authors have attempted to address it [50]
BHARAT integrates brain structural, neurochemical, and behavioral data from magnetic resonance imaging, magnetic resonance spectroscopy, and neuropsychological testing, providing feature selection and ensemble-based classification. This framework’s focus is on AD classification through DL methods, and on determining relevant information originating from the analysis of multi-modal integrated data, such as early diagnostic biomarkers for AD pathogenesis [79]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the perspective of ND as a big data issue, such diverse observations could be pulled together to provide a personalized, multi-layer representation of patients, which considers the complex heterogeneity of the disease and the availability of effective diagnostic criteria and drug development deliverables. In this context, computational modeling and simulation represented key components of the scientific method in which both reductionist and holistic approaches are not treated as separate fields but as convergent and cross-supportive paths [7,8,9,12]. Publicly available databases collecting multiple sources of biomedical information for ND will be reviewed
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