deep learning

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 community assessment to benchmark the ability of 24 representative network-based methods to predict protein-protein interactions across five different biological network, including a synthetic network, and the network of four different organisms: A. thaliana (plant), C. elegans (worm), S. cerevisiae (yeast), and H. sapiens.

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.

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.

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.

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

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