Ethan Pickering

Sequence-based modeling of plant epigenomes reveals cell-type-specific cis-regulatory grammar

Biology‐informed neural networks learn nonlinear representations from omics data to improve genomic prediction and biological discovery

Here, BINN is extended for genomic prediction and selection in crops by integrating thousands of single‐nucleotide polymorphisms with multi‐omics measurements and prior biological knowledge and substantially reduces prediction error relative to conventional neural nets and correctly identifies the most important nonlinear pathway.

CASCADE recovers promoter-associated regulatory motifs from cell-type-resolved DNA language-model attributions

ContextAware Significance of Cross-gene Attribution for Discovering Elements (CASCADE) is introduced, a positionspecific statistical framework for identifying model-derived candidate regulatory elements from in silico saturation mutagenesis and shifts motif recovery from downstream of the transcription start site toward promoter sequence.