Gianluca Lombardi

This paper introduces ILMC, a sampling strategy for protein language models that improves diversity while steering generation toward desired properties.

The paper analyzes the genomic structure and evolution of the S-locus in apple to show how balancing selection maintains conserved S-haplotype architectures despite structural divergence.

The paper analyzes olfactory and gustatory receptor genes and transposable elements in aphid genomes to understand their evolutionary dynamics and role in host adaptation.

LoRA-DR-suite predicts intrinsic and soft disorder in proteins using adapter-based embeddings, with SoftDis providing a database of 500,000 PDB chains.

MuLAN predicts protein interactions and binding affinity changes from sequence data using a deep learning approach without requiring structural information.

This paper introduces GEMME, a method for predicting mutational effects using sequence data and enhances it with structural and allele frequency data to improve biological insights.