Carlo Vittorio Cannistraci

This paper introduces Network Shape Intelligence (NSI) and shows it outperforms AlphaFold2 in predicting protein interactions using only local network information without training.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper shows that threatening faces are better remembered than non-threatening ones after 1.5 years, with differential neural encoding in specific brain regions.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

The paper introduces a network automaton rule, CH2-L3, to predict complex network structures across different domains like protein interactomes and food webs by maximizing internal links in local communities.

LIPEA is a web tool that performs over-representation analysis on lipid signatures to identify significantly enriched biological pathways from lipidomics data.