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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Grau, I., et al.: GeVaCT: Genomic Variant Classifier Tool. http://bridgeiris.ulb.ac.be:81/gevact/
Acknowledgments
The authors acknowledge the support of the BridgeIRIS project funded by INNOVIRIS, Brussels, Belgium and the Cuba-Flanders VLIR Network.
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Grau, I. et al. (2018). Genomic Variant Classifier Tool. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_32
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DOI: https://doi.org/10.1007/978-3-319-56994-9_32
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