Nicole Bussola

The paper introduces a diploid genomic foundation model to represent diploid genotypes in genomic language models, improving variant discrimination in compound-heterozygous cases.

Wrap a genomic foundation model's inference methods once to score it on supervised and zero-shot GUE, VariantBenchmarks, BEND, TraitGym and ClinVar tasks; code is promised upon acceptance.

The paper introduces INF, a multi-omics framework that improves cancer classification by integrating gene expression, protein, and copy number data, achieving better accuracy and smaller feature sets than single-omics models.

The paper introduces a framework combining deep learning and radiomics for cancer prognosis using CT and PET imaging, showing improved accuracy over single-method approaches.

This study evaluates the reproducibility of AI algorithms in digital pathology using the DAPPER framework, analyzing accuracy and feature stability across multiple deep learning models and datasets.