Faggioli G, Menotti L, Marchesin S, Trescato I, Ahmad L, Aidos H, Alungulese AL, Bellazzi R, Bergamaschi R, Birolo G, Bosoni P, Cabrera-Umpierrez MF, Cavalla P, Chiò A, Dagliati A, de Carvalho M, Fariselli P, García Domínguez JM, Gonzalez-Martinez S, Gromicho M, Guazzo A, Jovanovic A, Kostic B, Longato E, Madeira S, Manera U, Muñoz Blanco JL, Tavazzi El, Tavazzi E, Trasobares Iglesias E, Uroševic V, Di Nunzio GM, Silvello G, Di Camillo B, Ferro N, and Vettoretti M. The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling, Sci. Data, 2025
Lee Y, Baruzzo G, Kim J, Seo J, Di Camillo B. Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data. IEEE Access 2025
Cesaro G, Baruzzo G, Tussardi G, Di Camillo B. Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data. NAR Genomics and Bioinformatics 2025
Cesaro G, Nagai JS, Gnoato N, Chiodi A, Tussardi G, Klöker V, Musumarra CV, Mosca E, Costa IG, Di Camillo B, Calura E, & Baruzzo G. Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions. Briefings in Bioinformatics 2025
New Review on Cell–Cell Communication Published in Briefings in Bioinformatics!
We are excited to share our latest review article, “Advances and challenges in cell–cell communication inference: a comprehensive review of tools, resources, and future directions”, now published in Briefings in Bioinformatics:
📖 https://doi.org/10.1093/bib/bbaf280
This review offers a detailed overview of the current landscape of computational methods and databases used to infer cell–cell communication.
Key highlights include:
🔍 Analysis of 26 ligand-receptor resources, highlighting differences in coverage, species, signaling types, etc-
🔍 Classification of 58 bioinformatics tools for CCC inference, evaluated by input data requirements, computational strategies (intercellular, intracellular, differential communication), and output characteristics, as well as implementation choices
🔍 It also features a curated online catalog of CCC resouces and tools, designed to support researchers in identifying the most appropriate methods based on their data and analysis goals: https://sysbiobig.gitlab.io/ccc-catalog
This work is a collaboration between groups from the Departments of Information Engineering and Biology at the University of Padova, the Institute of Biomedical Technologies at the National Research Council (CNR), and the RWTH Aachen Medical Faculty’s Institute for Computational Genomics.