Carlo Piermarocchi

This paper identifies the LIF-LIFR axis as a key regulator of liver regeneration through LSECs, promoting angiogenesis and HGF release, and suggests therapeutic strategies for liver repair.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

This study analyzes time-resolved biochemical and immune measurements from 27 astronauts before, during, and after spaceflight to identify physiological changes linked to spaceflight conditions.

The paper uses differential network analysis to study gene expression changes in response to vaccination and drug treatment, identifying pathway activations consistent with the mechanisms of action.

The paper develops a method to analyze longitudinal multi-omics profiles of prediabetic and diabetic individuals to identify individual and group-level immune response patterns.

The paper identifies naturally occurring receptor combinations in endothelial cells using single-cell data and introduces a software tool to analyze them, showing evolutionary conservation and relevance to angiogenesis.

The paper introduces a visibility graph-based method to detect temporal communities in biological time series and validates it on simulated and experimental data.

The paper introduces DCS, a toolkit for scRNA-seq analysis that includes cell type identification, anomaly detection, and visualization tools, demonstrating its effectiveness on PBMC and plasma cell datasets.

This study tracks saliva omics changes over time in response to immune perturbation, revealing broad immune responses in both innate and adaptive timeframes.

PyIOmica integrates and classifies longitudinal omics datasets using tools for normalization, visualization, and pathway analysis.

The paper introduces a tool for automatically assigning cell types to scRNA-seq clusters using a voting algorithm based on molecular markers, validated on bone marrow data from the Human Cell Atlas.

The paper models cell cycle dynamics using Hopfield systems with gene expression data from HeLa and yeast cells, identifying gene inhibition targets to disrupt the cycle.