My Publications
2025
An Empirical Investigation of Shortcuts in Graph Learning
In Graph-Based Representations in Pattern Recognition (pp. 147–156). Springer Nature Switzerland.
Relating Explanations with the Inductive Biases of Deep Graph Networks
In AIxIA 2024 – Advances in Artificial Intelligence (pp. 175–187). Springer Nature Switzerland.
Analyzing Explanations of Deep Graph Networks through Node Centrality and Connectivity
In Discovery Science (pp. 295–309). Springer Nature Switzerland.
Towards Efficient Molecular Property Optimization with Graph Energy Based Models
In Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 289–294). i6doc.com.
Sensitivity analysis on Protein-Protein Interaction Networks through Deep Graph Networks
BMC Bioinformatics, 26(124).
A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens
PLOS ONE, 20(6), pp.1-22.
2024
Predictive machine learning model for mechanical dilatation in transvenous lead extraction procedures
European Heart Journal Supplements, 26(Supplement_2), pp.ii82–ii82.
Classifier-free graph diffusion for molecular property targeting
In 4th workshop on Graphs and more Complex structures for Learning and Reasoning - Colocated with AAAI 2024.
Explaining Graph Classifiers by Unsupervised Node Relevance Attribution
In Explainable Artificial Intelligence (pp. 63–74). Springer Nature Switzerland.
Classifier-Free Graph Diffusion for Molecular Property Targeting
In Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD (pp. 318–335). Springer Nature Switzerland.
How Much Do DNA and Protein Deep Embeddings Preserve Biological Information?
In Computational Methods in Systems Biology (pp. 209–225). Springer Nature Switzerland.
XAI and Bias of Deep Graph Networks
In Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 41–46). i6doc.com.
Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning
iScience, 27(3).
2023
Graph Representation Learning
In Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 1–10). i6doc.com.
Deep Graph Networks for Drug Repurposing with Multi-Protein Targets
IEEE Transactions on Emerging Topics in Computing, pp.1–14.
Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs
Bioinformatics, 39(11).
2022
Deep Learning in Cheminformatics
In Deep Learning in Biology and Medicine (pp. 157–195). World Scientific Publishing.
2021
A rigorous evaluation of embeddings-based vs. feature-based machine learning models for protein antigenicity prediction
In The 10th Italian Workshop on Machine Learning and Data Mining” (MLDM) – Part of the 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA).
Deep Learning on Graphs with Applications to the Life Sciences
GraphGen-Redux: A Fast and Lightweight Recurrent Model for labeled Graph Generation
In International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.
Classification of Biochemical Pathway Robustness with Neural Networks for Graphs
In Communications in Computer and Information Science (pp. 215–239). Springer International Publishing.
2020
A Fair Comparison of Graph Neural Networks for Graph Classification
In 8th International Conference on Learning Representations (ICLR).
A Deep Generative Model for Fragment-Based Molecule Generation
In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS).
Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS (pp. 32–43). SCITEPRESS.
Biochemical Pathway Robustness Prediction with Graph Neural Networks
In Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 121–126). i6doc.com.
Edge-based sequential graph generation with recurrent neural networks
Neurocomputing, 416, pp.177–189.
2019
Preliminary Results on Predicting Robustness of Biochemical Pathways through Machine Learning on Graphs
In Pre-proceedings of the 8th International Symposium “From Data to Models and Back (DataMod)”.
Graph generation by sequential edge prediction
In Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 95–100). i6doc.com.
2018
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor
Scientific Reports, 8(1).
2017
Predicting mortality in low birth weight infants: a machine learning perspective