Giuseppe Jurman

The paper develops a generative framework to synthesize missing modalities in multimodal datasets, validated on cancer data, showing it can maintain predictive model performance on incomplete profiles.

This study evaluates generative models for transcriptomics data using statistical distances to assess their performance and convergence.

This paper introduces singleDeep, a deep learning pipeline for scRNA-Seq data analysis that improves phenotype prediction and provides insights into gene importance and cell types.

This paper introduces Signature Distance, a statistical distance that improves upon energy distance for comparing empirical distributions in high-dimensional biological data, showing greater sensitivity to density changes and enabling model-free data expansion.

The paper analyzes transcriptomics data availability and reusability between microarray and RNA-seq experiments published between 2009 and 2013, and provides recommendations to improve RNA-seq data sharing.

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.

The paper introduces BiRewire 3, a tool for efficiently randomizing biological networks while preserving node functional characteristics, and demonstrates its application in network analysis.

CIDER identifies waves of coordinated gene transcription in time-course data by combining clustering and motif enrichment to detect regulatory interactions.

The paper introduces a null model for Pearson coexpression networks to assess statistical significance of correlations in gene expression data.