The HELIX AI framework integrates genomic sequence data with tissue-specific protein expression to accurately predict RNA splicing and transcript isoform usage across thirty human tissues. For tech professionals, this scalable deep-learning architecture utilizes LSTM networks to capture complex splice site dependencies, outperforming existing methods in identifying pathogenic variants and molecular signatures of cancer. Developers can leverage the scHELIX extension to profile intratumoral heterogeneity at single-cell resolution, potentially uncovering new therapeutic targets and improving patient stratification.
Read the full article at Genetic Engineering News
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