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Quantify genetic variants' regulatory potential via a hybrid sequence-oriented model

Wang, Y.; Liang, N.; Gao, G.

2024-04-01 bioinformatics
10.1101/2024.03.28.587115 bioRxiv
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Understanding how noncoding DNA determines gene expression is critical for decoding the functional genome. Leveraging a hybrid sequence-oriented architecture, we developed SVEN to model (and predict) tissue-specific transcriptomic impacts for large-scale structural variants across over 200 tissues and cell lines. We expect that SVEN will enable more effective in silico analysis and interpretation of human genome-wide disease-related genetic variants.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.