AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: SILVANA ALBERT.
DOI: 10.24193/subbi.2017.1.06
Published Online: 2017-06-01
Published Print: 2017-06-01
pp. 75-89
VIEW PDF: A BIG DATA APPROACH IN MUTATION ANALYSIS AND PREDICTION
Although the technology advancement in the last few years has been exponentially growing, there are still a lot of medical problems that don’t have an accessible solution. One of these problems is the one that genetics is facing: the absence of a solution for inspecting the previously reported genetic mutations. In order to confirm a mutation, the specialists need to narrow it down based on their experience and, if present,the few documented precedent cases. This paper focuses on presenting solution for analyzing big amounts of historical genetic data in an efficient, fast and user-friendly way. As a proof of concept, it demonstrates the huge role that Big Data has in genetic mutations aggregation and it can be considered a starting point for similar solutions that aim to continuously innovate genetics. The effectiveness of our proposal is highlighted by comparing it with similar existing solutions.
2010 Mathematics Subject Classification. 68N01, 68T05.1998 CR Categories and Descriptors. D.2.11 [Software]: Software engineering { Software Architectures; I.2.6[Computing Methodologies]: Artificial Intelligence { Learning.
Key words and phrases. Big data, genetics, software, machine learning.
