Rita Casadio

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

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

DDGemb predicts protein stability changes from single- and multi-point variations using embeddings and deep learning, achieving state-of-the-art performance.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

CoCoNat predicts coiled-coil boundaries, register annotations, and oligomerization states using deep learning, outperforming existing methods on multiple metrics.

ISPRED-SEQ predicts protein-protein interaction sites from sequences using deep neural networks and outperforms existing methods.

E-SNPs&GO improves pathogenic variant annotation by embedding protein sequences and function, achieving a MCC score of 0.71 on a blind test set.

NETGE-PLUS provides a web tool for standard and network-based gene enrichment analysis across human and model organisms, enabling functional interpretation of gene sets and pathway relationships.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.