Alberto Paccanaro

DCSE predicts drug combination side effects using a deep learning model, outperforming existing methods in realistic settings with limited data.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

This paper introduces a stereochemistry-aware framework for drug-target affinity prediction, showing improved accuracy in distinguishing enantiomer affinities.

This paper introduces F2S, a method that predicts drug side effect frequencies by accounting for chirality, achieving comparable performance to state-of-the-art methods with fewer false positives.

The paper trains AI models to predict RNA m5C sites using a curated dataset, achieving high accuracy and revealing the importance of data quality and biological validation.

This paper introduces Finenzyme, a Protein Language Model that improves EC category prediction through fine-tuning, generating functionally similar enzymes with preserved structure and function.

The paper identifies miR-2392 as a miRNA involved in SARS-CoV-2 infection, showing its role in inflammation, glycolysis, and hypoxia, and develops a miRNA-based therapeutic targeting it.

Inception predicts missing drug-target interactions using an interpretable self-expressive model, outperforming existing methods in cross-validation and prospective testing.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

The paper introduces a matrix completion model to predict drug side effects using pharmacological side information, achieving better performance than existing methods with explainable predictions.

A novel explainable machine learning algorithm predicts the frequency of drug side effects in the population.