Genetic Mapping of Diseases through Big Data Techniques

Julio Cesar Santos dos Anjos, Bruno Reckziegel Filho, Junior F. Barros, Raffael B. Schemmer, Claudio Geyer, Ursula Matte

Abstract

The development of sophisticated sequencing machines and DNA techniques has enabled advances to be made in the medical field of genetics research. However, due to the large amount of data that sequencers produce, new methods and programs are required to allow an efficient and rapid analysis of the data. MapReduce is a data-intensive computing model that handles large volumes that are easy to program by means of two basic functions (Map and Reduce). This work introduces GMS, a genetic mapping system that can assist doctors in the clinical diagnosis of patients by conducting an analysis of the genetic mutations contained in their DNA. As a result, the model can offer a good method for analyzing the data generated by sequencers, by providing a scalable system that can handle a large amount of data. The use of several medical databases at the same time makes it possible to determine susceptibilities to diseases through big data analysis mechanisms. The results show scalability and offer a possible diagnosis that can improve the genetic diagnosis with a powerful tool for health professionals.

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Paper Citation


in Harvard Style

Santos dos Anjos J., Reckziegel Filho B., F. Barros J., B. Schemmer R., Geyer C. and Matte U. (2015). Genetic Mapping of Diseases through Big Data Techniques . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 279-286. DOI: 10.5220/0005365402790286


in Bibtex Style

@conference{iceis15,
author={Julio Cesar Santos dos Anjos and Bruno Reckziegel Filho and Junior F. Barros and Raffael B. Schemmer and Claudio Geyer and Ursula Matte},
title={Genetic Mapping of Diseases through Big Data Techniques},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005365402790286},
isbn={978-989-758-096-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Genetic Mapping of Diseases through Big Data Techniques
SN - 978-989-758-096-3
AU - Santos dos Anjos J.
AU - Reckziegel Filho B.
AU - F. Barros J.
AU - B. Schemmer R.
AU - Geyer C.
AU - Matte U.
PY - 2015
SP - 279
EP - 286
DO - 10.5220/0005365402790286