Abstract

The Alzheimer’s Disease is one of the most prevalent neurological disorders in our current society. The study of the genetic characteristics of every patient, makes possible the study of significant DNA variations in order to ease an early diagnosis, essential to stop the progression of the disorder. The problem is that the vast amount of available information makes necessary the use of a method designed to adequately store and manage this data in an optimal way for its exploitation. In this context, the Information Systems Engineering in general and the conceptual modelling techniques in particular, provide a suitable solution in order to determine which data is relevant and how to manage the corresponding information. With these fundamentals in mind, this paper introduces a particular example to bear the methodological treatment of the search, filter and load of genomic variations related to Alzheimer’s Disease for its later exploitation with clinical purposes.

Recommended Citation

León, A., Pascual Fernández, I., & Pastor López, O. (2018). Genomic Information Systems applied to Precision Medicine: Genomic Data Management for Alzheimer’s Disease Treatment. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund University. ISBN: 978-91-7753-876-9. http://aisel.aisnet.org/isd2014/proceedings2018/eHealth/6.

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Genomic Information Systems applied to Precision Medicine: Genomic Data Management for Alzheimer’s Disease Treatment

The Alzheimer’s Disease is one of the most prevalent neurological disorders in our current society. The study of the genetic characteristics of every patient, makes possible the study of significant DNA variations in order to ease an early diagnosis, essential to stop the progression of the disorder. The problem is that the vast amount of available information makes necessary the use of a method designed to adequately store and manage this data in an optimal way for its exploitation. In this context, the Information Systems Engineering in general and the conceptual modelling techniques in particular, provide a suitable solution in order to determine which data is relevant and how to manage the corresponding information. With these fundamentals in mind, this paper introduces a particular example to bear the methodological treatment of the search, filter and load of genomic variations related to Alzheimer’s Disease for its later exploitation with clinical purposes.