Gherman Novakovsky

ExplaiNN provides interpretable deep learning models for genomics tasks, achieving comparable performance to state-of-the-art methods while offering transparent predictions.

The paper identifies small molecules that enhance stem cell differentiation into definitive endoderm, reducing costs by 90% through a drug repurposing approach.

The paper develops a cloud computing infrastructure for petabase-scale sequence alignment, identifying over 105 novel RNA viruses and expanding known species by an order of magnitude.

This study shows that transfer learning improves transcription factor binding prediction by pre-training with biologically relevant TFs and fine-tuning single-task models.

bioSyntax provides syntax highlighting tools for computational biology data formats to improve readability and efficiency.