Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence
<p>Common algorithm types for ML and DL employed in ND biomedical research.</p> "> Figure 2
<p>Number of publications in ML or DL fields by year and ND. Data were retrieved on dimensions.ai using Alzheimer’s or Parkinson’s diseases and deep learning or machine learning as keywords to search in title and abstract. Results were limited to “article” as Publication Type.</p> "> Figure 3
<p>Deep Learning handwritings classification. CN and PD handwritings are hard to distinguish if not trained to. A CNN can be made capable of classifying patients and controls upon almost imperceptible changes in subjects’ drawings. Convolution and pooling operations process input data to extract relevant features from the images, allowing detection of group differences. Spirals images were taken from the NewHandPD dataset [<a href="#B35-jpm-11-00280" class="html-bibr">35</a>], available at <a href="http://wwwp.fc.unesp.br/~papa/pub/datasets/Handpd/" target="_blank">http://wwwp.fc.unesp.br/~papa/pub/datasets/Handpd/</a>, accessed on 5 January 2021.</p> "> Figure 4
<p>A DL workflow implementing dimensionality reduction strategies to integrate large and heterogeneous datasets. Dimensionality reduction algorithms can be applied to standard multi-omics data, integrating different features from the same set of observations or obtaining one outcome variable from different layers of biological systems.</p> "> Figure 5
<p>Multi-layer picture of neurodegenerative diseases. Separated data can be integrated to obtain a holistic representation of patients. Artificial intelligence techniques application for data processing leads to useful findings in ND research, clinical management, and personalized treatment development.</p> ">
Abstract
:1. Introduction
Literature Research
2. Basics of Machine Learning and Deep Learning
3. Artificial Intelligence in Neurology
3.1. Neuroimaging Classification and Segmentation
3.2. Clinical Records Investigation
4. Big Data Integration
4.1. Multi-Omics
4.2. Electronic Health Records (EHRs)
4.3. Artificial Intelligence Applications on ND Multi-Omics and Clinical Data Integration
5. Databases
6. Challenges and Limitations for AI Techniques in ND Research
7. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Architecture | Description | Graph |
---|---|---|
Deep Neural Network (DNN) | The basic network is made of multiple hidden layers. It is capable of modeling complex non-linear relationships by learning input data representation to be matched with a specific output [19]. | |
Autoencoder (AE) | It allows detecting patterns in the data in an unsupervised fashion. The model is made of an encoder and a decoder, transforming input data to generate its own representation, aiming to minimize the difference between the input and its output representation [20]. | |
Restricted Boltzmann Machine (RBM) | This model is made of two layers, where nodes are bidirectionally connected but there are no connections within one layer. It is trained to learn a probability distribution for the input data and can be used as a building block for deep probabilistic models, where multiple RBMs can be stacked to build a deeper network [21]. | |
Convolutional Neural Network (CNN) | Most used for image processing in computer vision applications. The network uses convolution and pooling operations to extract relevant features from data, useful for image classification. This architecture is inspired by the organization of the visual cortex [22]. | |
Recurrent Neural Network (RNN) | Best suited to process sequential data and used to predict the future from the past. The network can give an output for every timestep and takes the previous inputs into account to determine the output. Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRUs) are RNN architectures [19]. |
Database Name | ND | URL | Data Type | Description |
---|---|---|---|---|
PDGene | PD | http://www.pdgene.org, accessed on 19 February 2021 | Omics | PDGene is a database providing results for potential risk loci in PD [61]. |
PPMI | PD | https://www.ppmi-info.org, accessed on 19 February 2021 | Mixed | The Parkinson’s Progression Markers Initiative holds a comprehensive set of clinical, imaging, and biosample data to define biomarkers of PD progression [25]. |
NIAGADS | AD | https://www.niagads.org, accessed on 19 February 2021 | Omics | The National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site is a repository that collects and shares genotypic data for the study of AD and related dementias [58]. |
ADNI | AD | http://adni.loni.usc.edu, accessed on 19 February 2021 | Mixed | The Alzheimer’s Disease Neuroimaging Initiative is a multisite study for the prevention and treatment of AD. Its database stores a collection of validated study data to define the progression of AD, including mild cognitive impairment subjects and elderly controls [60]. |
NACC | AD | https://www.alz.washington.edu, accessed on 19 February 2021 | Clinical | The National Alzheimer’s Coordinating Center holds a large relational database of standardized clinical and neuropathological research data for both exploratory and explanatory AD research [65]. |
LAADC | AD | https://www.ohsu.edu/brain-institute/clinical-data-resources, accessed on 19 February 2021 | Clinical | Longitudinal relational database from the Layton Aging and Alzheimer’s Disease Center holding clinical data for over 4000 research subjects. |
GEO | Mixed | http://www.ncbi.nlm.nih.gov/geo, accessed on 19 February 2021 | Omics | Gene Expression Omnibus is a public functional genomics data repository of array-and sequence-based data [62]. |
UK Biobank | Mixed | https://www.ukbiobank.ac.uk, accessed on 19 February 2021 | Omics | UK Biobank contains data from a large prospective study with over 500,000 participants and it aims to improve the prevention, diagnosis, and treatment of various illnesses, including dementia [63]. |
OmicsDI | Mixed | https://www.omicsdi.org/, accessed on 19 February 2021 | Omics | Omics Discovery Index facilitates access to omics datasets from multiple studies through an integrated and open-source platform [57]. |
JPND | Mixed | https://www.neurodegenerationresearch.eu, accessed on 19 February 2021 | Mixed | The Joint Programme Neurodegenerative Disease Research Database contains data from research related to neurodegenerative diseases from 27 member countries. |
GAAIN | Mixed | http://www.gaaindata.org, accessed on 19 February 2021 | Mixed | The Global Alzheimer’s Association Interactive Network is an online integrated research platform affiliated with partners all over the world, providing resources and data enabling comparative data analysis and cohort discovery [59]. |
Bio FINDER | Mixed | https://biofinder.se/, accessed on 19 February 2021 | Mixed | The Swedish Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably study aims to develop early diagnostic tests to identify novel treatment targets and understand the links between different ND and clinical symptoms. |
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Termine, A.; Fabrizio, C.; Strafella, C.; Caputo, V.; Petrosini, L.; Caltagirone, C.; Giardina, E.; Cascella, R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. J. Pers. Med. 2021, 11, 280. https://doi.org/10.3390/jpm11040280
Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Giardina E, Cascella R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. Journal of Personalized Medicine. 2021; 11(4):280. https://doi.org/10.3390/jpm11040280
Chicago/Turabian StyleTermine, Andrea, Carlo Fabrizio, Claudia Strafella, Valerio Caputo, Laura Petrosini, Carlo Caltagirone, Emiliano Giardina, and Raffaella Cascella. 2021. "Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence" Journal of Personalized Medicine 11, no. 4: 280. https://doi.org/10.3390/jpm11040280