<ul data-eligibleForWebStory="false"><li>Researchers propose a transformer-based matrix variational auto-encoder (matVAE) for variant effect prediction in pharmacogenes, dealing with low evolutionary pressure in pharmacogenomics.</li><li>The model, matVAE-MSA, outperforms the DeepSequence model in zero-shot prediction on deep mutational scanning (DMS) datasets, requiring fewer parameters and less computation at inference time.</li><li>Comparison with a model trained on DMS data, matENC-DMS, shows the latter performs better on supervised prediction tasks.</li><li>Incorporating AlphaFold-generated structures into the transformer model boosts performance, paving the way for leveraging DMS datasets to enhance variant effect prediction without significant loss in predictive accuracy.</li></ul>

A Matrix Variational Auto-Encoder for Variant Effect Prediction in Pharmacogenes

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