Genomic Variant Classifier Tool

Isel Grau, Dipankar Sengupta, Dewan Md Farid, Bernard Manderick, Ann Nowe, Maria M. Garcia Lorenzo, Dorien Daneels, Maryse Bonduelle, Didier Croes, Sonia Van Dooren

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of SAI Intelligent Systems Conference (IntelliSys) 2016
PublisherSpringer, Cham
Number of pages4
ISBN (Electronic)978-3-319-56994-9
ISBN (Print)978-3-319-56993-2
Publication statusPublished - 20 Aug 2017

Publication series

NameLecture Notes in Networks and Systems
PublisherSpringer, Cham
ISSN (Print)2367-3370

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