Article

Machine Learning Models for Accurate Prioritization of Variants of Uncertain Significance

2020-11-25

Abstract excerpt

The growing use of new generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of Variants of Uncertain Significance (VUS). In this manuscript we compare three machine learning methods to classify VUS as Pathogenic or No pathogenic, implementing a Random Forest (RF), a Support Vector Machine (SVM), and a Multilayer Perceptron (MLP). To train the models, we extract...

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Literature Corpus work: bfb3e610-60dc-5fb9-9459-b532ed05ba6e
DOI: 10.22541/au.160629133.32270917/v1

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