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Variant classification in the sequencing era
Furthermore, they can influence pharmacogenetics,48 and the effect of the same somatic mutation may differ between patients depending on other acquired or inher- ited genetic factors.
Artificial intelligence is a logical choice to exploit the full potential of the data and leave the binary mutation/polymorphism classification behind. The use of artificial intelligence in clinical oncology, genome inter- pretation, and especially in variant reporting has gained momentum.49,50 Data available from manually classified variants can be used to train deep neural networks.49 The advantage of this approach is that the algorithm is able to autonomously extract relevant features for classification
and identify important combinations not only for genetic information but for all types of biomarker. There is no need for any manually defined set of rules. This is espe- cially useful for variant interpretation because, as described above, it is basically impossible to capture the entire complexity using a simple set of rules. The output of such algorithms could indicate clinically relevant likeli- hoods. However, in order to aid clinical decision-making, a future report should not just be a list of variants with their individual classifications but rather a personalized summary of all genetic information (including structural and copy number variations) and other biomarkers and their combined meaning.
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