Guido Uguzzoni

This paper introduces a scalable algorithm to calculate exact discovery probabilities in mutagenesis libraries and provides an open-source tool for experimental design.

This paper benchmarks synthetic data generation methods for clinical transcriptomic cancer data, finding no single method outperforms all metrics, with Gaussian Copula showing the most balanced performance.

The paper introduces simple autoregressive models for efficient generative modeling of protein sequences, showing comparable performance to complex models at lower computational cost.

The paper introduces a computational method to infer protein fitness landscapes from deep mutational scans, showing accurate predictions and structural insights.

This study shows that coupling matrices from DCA contain quantitative physico-chemical information about amino-acid interactions, validated using synthetic data.