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Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

Based on a set of subjects and a collection of attributes obtained from the Alzheimer’s Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer’s disease (AD). We ex...

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Autores principales: Mihelčić, Matej, Šimić, Goran, Babić Leko, Mirjana, Lavrač, Nada, Džeroski, Sašo, Šmuc, Tomislav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663625/
https://www.ncbi.nlm.nih.gov/pubmed/29088293
http://dx.doi.org/10.1371/journal.pone.0187364
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author Mihelčić, Matej
Šimić, Goran
Babić Leko, Mirjana
Lavrač, Nada
Džeroski, Sašo
Šmuc, Tomislav
author_facet Mihelčić, Matej
Šimić, Goran
Babić Leko, Mirjana
Lavrač, Nada
Džeroski, Sašo
Šmuc, Tomislav
author_sort Mihelčić, Matej
collection PubMed
description Based on a set of subjects and a collection of attributes obtained from the Alzheimer’s Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer’s disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer’s Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.
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spelling pubmed-56636252017-11-09 Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients Mihelčić, Matej Šimić, Goran Babić Leko, Mirjana Lavrač, Nada Džeroski, Sašo Šmuc, Tomislav PLoS One Research Article Based on a set of subjects and a collection of attributes obtained from the Alzheimer’s Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer’s disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer’s Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly. Public Library of Science 2017-10-31 /pmc/articles/PMC5663625/ /pubmed/29088293 http://dx.doi.org/10.1371/journal.pone.0187364 Text en © 2017 Mihelčić et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mihelčić, Matej
Šimić, Goran
Babić Leko, Mirjana
Lavrač, Nada
Džeroski, Sašo
Šmuc, Tomislav
Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients
title Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients
title_full Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients
title_fullStr Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients
title_full_unstemmed Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients
title_short Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients
title_sort using redescription mining to relate clinical and biological characteristics of cognitively impaired and alzheimer’s disease patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663625/
https://www.ncbi.nlm.nih.gov/pubmed/29088293
http://dx.doi.org/10.1371/journal.pone.0187364
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