Angelica Lam

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

This paper develops interpretable biophysical neural networks to predict transcriptional activation domains from protein sequences, revealing how residue properties influence both activation strength and protein abundance.

Evolutionary turnover of key amino acids explains conservation of function without conservation of sequence in transcriptional activation domains

The paper shows that function can be conserved in transcriptional activation domains without sequence conservation through evolutionary turnover of key residues.

Evolutionary discovery and characterization of fungal transcriptional activators using active learning

The paper develops ADhunter, a model to identify and quantify transcriptional activators in fungal genomes, expanding functional annotations across non-model species.