Alessandro Lussana

@alussana.bsky.social

Bioinformatics Scientist. Delivering therapies to rare disease patients with data-driven discovery and decision making. https://alussana.net

We present SELPHI 2.0 a machine learning model integrating >40 sequence, omics and structural features to predict kinase-substrate interactions between 420 kinases and 240K phosphosites and improve interpretation of global phosphoproteomics data www.sciencedirect.com/science/arti...

Data-driven extraction of human kinase-substrate relationships from omics datasets

Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as cell division …

sciencedirect.com

Update: Since we published "Cracking the black box of deep sequence-based protein-protein interaction prediction," we have received mostly positive feedback. I presented our work at multiple conferences, including the ISMB/ECCB23 and GCB23. 🧬🖥️ doi.org/10.1093/bib/...

Cracking the black box of deep sequence-based protein–protein interaction prediction

Abstract. Identifying protein–protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed a

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