Use of GeIS for Early Diagnosis of Alcohol Sensitivity

Román, José Fabián Reyes; López, Oscar Pastor

doi:10.5220/0005822902840289

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Evolution of the CSHG resulted in five views: structural, variation, transcription, proteome, bibliography, and pathway. Using conceptual views allows us to focus on specific dimensions of interest, which have many practical uses such as identifying and managing genomic variations related to the treatment of Alzheimer's [9], developing a conceptual model-based framework to improve data quality processes for precision medicine [10], of reporting early diagnosis of alcohol sensitivity [14].…”

Section: Conceptual Schema Of the Genome (Cshg)mentioning

confidence: 99%

An Ontological Characterization of a Conceptual Model of the Human Genome

Guizzardi

Pastor

et al. 2022

Lecture Notes in Business Information Processing

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The ability to sequence the human genome is a scientific, historical breakthrough. Although the human genome mapping is available to all scientists, information about it can be difficult to share. The Conceptual Schema of the Human Genome represents the concepts required to holistically understand the human genome. We report on our continued efforts to ensure that the human genome can be meaningfully shared by conducting an ontological analysis and enrichment of the conceptual model to facilitate domain understanding and data exchange among heterogeneous systems. The analysis and enrichment process is supported by the ontology-driven conceptual modeling language, OntoUML, to gain ontological clarity and demonstrated on a relevant section of the Pathways view of the schema. Consistent with the overall objective of designing a sound genomics information system, the results lead to major modeling implications for the: characterization of biological entities; changes in biological entities over time; and representation of chemical compounds. Our research shows that the inclusion of a strong ontological foundation in a conceptual model contributes to the design of complex systems.

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Section: Conceptual Schema Of the Genome (Cshg)mentioning

confidence: 99%

An Ontological Characterization of a Conceptual Model of the Human Genome

Guizzardi

Pastor

et al. 2022

Lecture Notes in Business Information Processing

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“…Table 1 shows the special data characteristics of this domain in terms of Big Data dimensions. A systematic way to perform the DGD is the SILE methodology [13], which consists of four levels: Search (the researchers search for external biological databases and select those that serve the analysis purposes), Identification (for each biological database, the researchers identify the content suitable for the analysis), Load (the VCF files and the biological databases content are loaded into a central repository) and Exploitation (the researchers explore, operate, analyze, and extract knowledge from genetic data by drawing conclusions from them).…”

Section: The Diagnosis Of Genetic Diseases (Dgd)mentioning

confidence: 99%

Defining Interaction Design Patterns to Extract Knowledge from Big Data

Iñiguez-Jarrín

Panach

López

2018

Advanced Information Systems Engineering

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The Big Data domain offers valuable opportunities to gain valuable knowledge. The User Interface (UI), the place where the user interacts to extract knowledge from data, must be adapted to address the domain complexities. Designing UIs for Big Data becomes a challenge that involves identifying and designing the user-data interaction implicated in the knowledge extraction. To design such an interaction, one widely used approach is design patterns. Design Patterns describe solutions to common interaction design problems. This paper proposes a set of patterns to design UIs aimed at extracting knowledge from the Big Data systems' data conceptual schemas. As a practical example, we apply the patterns to design UI's for the Diagnosis of Genetic Diseases domain since it is a clear case of extracting knowledge from a complex set of genetic data. Our patterns provide valuable design guidelines for Big Data UIs.

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“…After completing the search and identification processes [38], we turned to the medical validation of genes, with the help of our fellow biologists (supporting this validation with papers from high-impact medical journals). We proceed to filter the genes in Table 1, selecting only three relevant genes: (a) ALDH2 (rs671); (B) GABRA2 (rs279836, rs279871) and (c) ADH1B (rs1229982, rs1229984).…”

Section: Background: Understanding the Haplotype Concept -Test Case: mentioning

confidence: 99%

“…We detected a "haplotype case" with the GABRA2 gene, in which we found a haplotype composed of three variants (variations): rs279871, rs279836 and rs279845 [38]. Initially we worked with individual variations 1 , without considering the relationships between them and without considering the rs279845 variant [11][12]).…”

Section: Background: Understanding the Haplotype Concept -Test Case: mentioning

confidence: 99%

How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

Román

Pastor

Martínez

et al. 2016

CLEIej

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The goal of this work is to describe the advantages of the application of Conceptual Modeling (CM) in complex domains, such as genomics. Nowadays, the study and comprehension of the human genome is a major challenge due to its high level of complexity. The constant evolution in the genomic domain contributes to the generation of ever larger amounts of new data, which means that if we do not manage it correctly data quality could be compromised (i.e., problems related with heterogeneity and inconsistent data). In this paper, we propose the use of a Conceptual Schema of the Human Genome (CSHG), designed to understand and improve our ontological commitment to the domain and also extend (enrich) this schema with the integration of a novel concept: Haplotypes. Our focus is on improving the understanding of the relationship between genotype and phenotype, since new findings show that this question is more complex than was originally thought. Here we present the first steps in our data management approach with haplotypes (variations, frequencies and populations) and discuss the database evolution to support this data. Each new version in our conceptual schema (CS) introduces changes to the underlying database structure that has essential and practical implications for better understanding and managing the relevant information. A solution based on conceptual models gives a clear definition of the domain with direct implications in the medical field (Precision Medicine), in which Genomic Information Systems (GeIS) play a very important role.

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Use of GeIS for Early Diagnosis of Alcohol Sensitivity

Cited by 10 publications

References 5 publications

An Ontological Characterization of a Conceptual Model of the Human Genome

An Ontological Characterization of a Conceptual Model of the Human Genome

Defining Interaction Design Patterns to Extract Knowledge from Big Data

How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

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