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.
|Title of host publication||Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016|
|Number of pages||4|
|Publication status||Published - 20 Aug 2017|
|Name||Lecture Notes in Networks and Systems|