Parnal Joshi

This paper introduces EnzPlacer, a contrastive learning method that predicts enzyme function from sequence by placing it in known function space, even when the precise function is unknown.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

This work analyzes CAFA5 submissions, showing improved accuracy in protein function prediction and highlights the role of protein language models and 3D structures in performance gains.

EpicTope predicts and validates epitope tagging sites by analyzing structural and functional features, showing effectiveness in zebrafish proteins.

This paper develops zebrafish hand2 Cre and CreERT2 drivers using CRISPR-Cas9 to label mesodermal and ectodermal lineages, revealing lineage contributions to various tissues and vasculature.

The paper analyzes function annotation biases in the human proteome over time, revealing persistent inequality in knowledge distribution and a gap between annotation and scientific interest.

GOThresher removes annotation biases from protein function annotation datasets to improve the quality of data for machine learning models.

The paper uses GOMAP to annotate gene functions across 18 crop plant genomes, showing that these annotations can recover known phylogenetic relationships, supporting comparative functional genomics.

The paper introduces MENTHU, a tool predicting MMEJ-repaired genotypes in zebrafish, showing improved sensitivity over inDelphi and validating its use for gene editing applications.