Abstract
Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing.
Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study.
This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.
Keywords: Alzheimer's Disease, Feature selection, Machine learning, Magnetic resonance imaging, Social network analysis.
Current Topics in Medicinal Chemistry
Title:Machine Learning and Social Network Analysis Applied to Alzheimer's Disease Biomarkers
Volume: 13 Issue: 5
Author(s): Javier Di Deco, Ana M. Gonzalez, Julia Diaz, Virginia Mato, Daniel Garcia–Frank, Juan Alvarez–Linera, Ana Frank and Juan A. Hernandez–Tamames
Affiliation:
Keywords: Alzheimer's Disease, Feature selection, Machine learning, Magnetic resonance imaging, Social network analysis.
Abstract: Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing.
Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study.
This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.
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Cite this article as:
Di Deco Javier, M. Gonzalez Ana, Diaz Julia, Mato Virginia, Garcia–Frank Daniel, Alvarez–Linera Juan, Frank Ana and A. Hernandez–Tamames Juan, Machine Learning and Social Network Analysis Applied to Alzheimer's Disease Biomarkers, Current Topics in Medicinal Chemistry 2013; 13 (5) . https://dx.doi.org/10.2174/1568026611313050008
DOI https://dx.doi.org/10.2174/1568026611313050008 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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