This study focuses on the importance of Genomic Information Systems (GeIS) today; the results of this research provide great benefits to the medical community through technological potential. The development of SILE (Search-Identification-Load-Exploitation) to GeIS improves the databases management with curated data. The studies are focused on improving the quality of data and time optimization. With SILE we perform a selective loading of genes and variations found for a specific disease from different data sources like: NCBI, dbSNP and others. When we worked with a selected group of genes/variations it is possible guaranteeing a more reliable diagnosis, thus sustaining the increase accuracy of the results with respect to data quality and improvements over time. Also, we integrate the association of genes/variations with population studies, for this way providing an early diagnosis for any disease of genetic origin.
The objective of this work was to enhance personalized medicine through the development and implementation of Genomic Information Systems (GeIS). For this, a web application called "GenesLove.Me" (GLM) was developed to provide direct-to-consumer genetic tests (DCGT). This paper focuses on the development of the business processes (BPMN) and a conceptual model (CM) for GLM, designed to analyze and improve the processes involved in this type of service and provide a model-based platform to manage genetic diagnoses in a scalable, secure and reliable way. Software Engineering (SE) approaches applied to the genomic context play a key role in the advancement of personalized and precision medicine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.