graph mining

A presentation of the current research projects of the lab for the new students of the Computer Science department.

A presentation of the current research projects of the lab.

A comprehensive overview of fundamental concepts, deep learning methods and critical challenges of Network Biology in Network Biology, Network Medicine, and Network Pharmacology.

Developed a novel deep graph learning method for drug-target interaction prediction. Demonstrated an application of our method to repurpose existing drugs against the COVID-19 virus.

A comprehensive overview of fundamental concepts, deep learning methods and critical challenges of Network Biology in Network Biology, Network Medicine, and Network Pharmacology.

Presented a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between disease modules. We compared the interactome-induced disease taxonomy with a human-curated disease taxonomy.

Authored a chapter in the textbook Deep Learning in Biology and Medicine. In the chapter we introduced the fundamental concepts of Network Biology, and we presented the state-of-the-art deep learning strategies used to tackle critical challenges in Network Biology, Network Medicine, and Network Pharmacology.

Proposed a novel machine learning methodology to shed light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of their disease module in the human interactome.

Developed a methodology and related algorithms to automatically induce a hierarchical structure of disease modules from proximity relations in the interactome network, and to align, label, and systematically compare this structure with a manually defined disease ontology.

Developed a deep graph learning-based model for predicting disease-gene associations. The model jointly learns functional and connectivity patterns using a novel technique based on attributed random walks.