Converting Alzheimer’s Disease Map into a Heavyweight Ontology: A Formal Network to Integrate Data

Henry, Vincent; Moszer, Ivan; Dameron, Olivier; Potier, Marie‐Claude; Hofmann-Apitius, Martin; Colliot, Olivier

doi:10.1007/978-3-030-06016-9_20

Cited by 10 publications

(9 citation statements)

References 20 publications

(22 reference statements)

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“…Efforts to interpret sequence data obtained from the brains of patients are underway, and newer methods and hypotheses have been proposed to understand the pathogenesis of neurological diseases (Bizzotto & Walsh, 2022;Exposito-Alonso & Rico, 2022;Henry et al, 2021;Vilela et al, 2022;Wilfert et al, 2017).…”

Section: Variation In the Neuronal Genome Contributes To Neurological...mentioning

confidence: 99%

“…A report showed that DNMs distribute unevenly in several to 100 kilobases in neurons of autistic brains, suggesting examination of genome‐wide characteristics in neurons may give us hints in information regarding a variable genome in neurodevelopmental disorders (Michaelson et al, 2012). Efforts to interpret sequence data obtained from the brains of patients are underway, and newer methods and hypotheses have been proposed to understand the pathogenesis of neurological diseases (Bizzotto & Walsh, 2022; Exposito‐Alonso & Rico, 2022; Henry et al, 2021; Vilela et al, 2022; Wilfert et al, 2017).…”

Section: Variation In the Neuronal Genome Contributes To Neurological...mentioning

confidence: 99%

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Genomic variation in neurons

Zolzaya,

Narumoto,

Katsuyama

2023

Dev Growth Differ

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Neurons born during the fetal period have extreme longevity and survive until the death of the individual because the human brain has highly limited tissue regeneration. The brain is comprised of an enormous variety of neurons each exhibiting different morphological and physiological characteristics and recent studies have further reported variations in their genome including chromosomal abnormalities, copy number variations, and single nucleotide mutations. During the early stages of brain development, the increasing number of neurons generated at high speeds has been proposed to lead to chromosomal instability. Additionally, mutations in the neuronal genome can occur in the mature brain. This observed genomic mosaicism in the brain can be produced by multiple endogenous and environmental factors and careful analyses of these observed variations in the neuronal genome remain central for our understanding of the genetic basis of neurological disorders.This article is protected by copyright. All rights reserved.

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Section: Variation In the Neuronal Genome Contributes To Neurological...mentioning

confidence: 99%

Section: Variation In the Neuronal Genome Contributes To Neurological...mentioning

confidence: 99%

Genomic variation in neurons

Zolzaya,

Narumoto,

Katsuyama

2023

Dev Growth Differ

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“…The study begins by examining the taxonomic process using the taxonomic levels in [30,31]. This is an ordering system that allows a set of elements to be grouped within certain predefined categories.…”

Section: Taxonomymentioning

confidence: 99%

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

et al. 2023

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The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer’s patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer’s disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest.

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“…ADO has been designed to be useful for ontology-driven searching of the literature. In [31], the AD Map Ontology (ADMO) is proposed, in order to describe knowledge about AD related biological pathways, based on systems biology terms. In [32], ontologies related to the area of neurodegenerative disease are presented, but the authors claim that all these ontologies have not be maintained over the time.…”

Section: Ad Data Collections and Ontologies: A Way For Data Harmoniza...mentioning

confidence: 99%

An Ontology-based approach for Modelling and Querying Alzheimer’s Disease Data

Taglino

Cumbo

Antognoli

et al. 2022

Preprint

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Background: The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being “big” enough and “standardized” so to be integrated, making impossible to fully exploit latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. An effective strategy to address this issue could be the building of a formal conceptual model, which in general allows the design of semantic tools to collect and explore data for a given pathology. This is the case of neurodegenerative diseases and the Alzheimer Disease (AD), in particular. The last years have witnessed the creation of specialized data collections such as the one maintained by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which contains the largest number of biomedical concepts in the field of AD. For this class of diseases Big Data and Deep Learning give hope for the discovery of new biomarkers for early diagnosis. Hence, a new way must be undertaken of collecting and managing biomedical data. Results: We developed a detailed ontology for clinical multidimensional datasets from the ADNI repository in order to simplify the data access, to obtain new diagnostic knowledge about Alzheimer’s disease and ease the task of harmonization. Conclusions: The semantic data base, populated by ADNI data will suggest new queries allowing machine learning techniques to be applied to any possible combination of data set. The conceptual model could be adopted by any center, so giving rise to new databases that, if made public available, will simplify data integration and multi center data collection projects.

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Converting Alzheimer’s Disease Map into a Heavyweight Ontology: A Formal Network to Integrate Data

Cited by 10 publications

References 20 publications

Genomic variation in neurons

Genomic variation in neurons

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

An Ontology-based approach for Modelling and Querying Alzheimer’s Disease Data

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