Sean David Mooney

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.

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

The paper uses single-cell RNA sequencing to study Huntington's disease by analyzing medium spiny neuron development in HD iPSC models and identifies potential therapeutic molecules.

The paper performs proteomic analysis of Huntington's disease patient-derived medium spiny neurons, identifying altered lipid metabolism and lipid droplet accumulation as key findings.

The paper calibrates computational tools for missense variant pathogenicity classification and provides evidence-based revisions to PP3/BP4 ACMG/AMP criteria.

The paper identifies Foxf2 and Olig2 as key transcription factors regulating striatal compartmentation and neuronal differentiation through RNA- and ATAC-seq analysis of murine striosomes and matrix.

MutPred2 identifies pathogenic amino acid variants, predicts their molecular mechanisms, and validates their functional impact in neurodevelopmental disorders.

Leaf is a web application that enables querying and extracting clinical data from multiple sources without requiring specialized informatics expertise.

The paper identifies 11 novel Drosophila genes involved in long-term memory formation using computational methods and behavioral assays.

EXPLANe annotates scientific posters with genetic variant information using mobile devices and optical text recognition.