Nathan Gravel

CLASPP: A unified model for predicting post-translational modifications

CLASPP is a unified deep learning model that predicts multiple PTM types from protein sequences, addressing data imbalance and improving prediction performance across organisms.

Identification and classification of ion-channels across the tree of life provide functional insights into understudied CALHM channels

The paper identifies and classifies ion channels across species, revealing functional insights into understudied CALHM channels through sequence, structure, and functional data integration.

Task Splitting and Prompt Engineering for Cypher Query Generation in Domain-Specific Knowledge Graphs

This paper introduces Prompt2Cypher, a method that uses task splitting and prompt engineering to improve Cypher query generation for domain-specific knowledge graphs, showing better accuracy in biological datasets.

Dark kinase annotation, mining and visualization using the Protein Kinase Ontology

The paper extends the Protein Kinase Ontology (ProKinO) to uncover new insights about dark kinases, identifying PAK5 as a frequently mutated kinase in cancers and highlighting its potential in drug discovery.

An explainable unsupervised framework for alignment-free protein classification using sequence embeddings

This paper introduces an alignment-free protein classification framework using sequence embeddings to infer evolutionary relationships and functional motifs across diverse protein superfamilies.