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Genomic Variant Classifier Tool Host Publication: SAI Intelligent Systems Conference 2016 Authors: I. Del Carmen Grau Garcia, M. M. Garcia Lorenzo, D. Sengupta, D. Dewan Farid, B. Manderick, A. Nowé, D. Daneels, M. Bonduelle, D. Croes and S. Van Dooren Publisher: Springer Publication Date: Sep. 2016 ISBN: 978-3-319-56993-2
Abstract: The exome or genome based high throughput screening techniques are becoming a definitive criterion in the conventional clinical analysis of the genetic diseases. However, pathogenic classification of an identified variant, is still a manual and time consuming process for clinical geneticists. Thus, to facilitate the variant classification process, we have developed GeVaCT, a Java based tool that implements a classification approach based on the literature review of cardiac arrhythmia syndromes. Furthermore, the adoption of this automated knowledge engineer by the clinical geneticists will aid to build a knowledge base for the evolution of the variant classification process by use of novel machine learning approaches.
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