Alexander Karollus

Oct 14, 2024 | biorxiv.org | Avantika Lal |Alexander Karollus |Laura M Gunsalus |David Garfield

AbstractSequence-to-function models that predict gene expression from genomic DNA sequence have proven valuable for many biological tasks, including understanding cis-regulatory syntax and interpreting non-coding genetic variants. However, current state-of-the-art models have been trained largely on bulk expression profiles from healthy tissues or cell lines, and have not learned the properties of precise cell types and states that are captured in large-scale single-cell transcriptomic datasets.