Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Massidda, Riccardo; Geiger, Atticus; Icard, Thomas; Bacciu, Davide Causal Abstraction with Soft Interventions Conference Proceedings of the 2nd Conference on Causal Learning and Reasoning (CLeaR 2023), PMLR, 2023. Ferrari, Elisa; Gargani, Luna; Barbieri, Greta; Ghiadoni, Lorenzo; Faita, Francesco; Bacciu, Davide A causal learning framework for the analysis and interpretation of COVID-19 clinical data Journal Article In: Plos One, vol. 17, no. 5, 2022. Bacciu, Davide; Lisboa, Paulo J. G.; Sperduti, Alessandro; Villmann, Thomas Probabilistic Modeling in Machine Learning Book Chapter In: Kacprzyk, Janusz; Pedrycz, Witold (Ed.): pp. 545–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015, ISBN: 978-3-662-43505-2. Davide, Bacciu; A, Etchells Terence; JG, Lisboa Paulo; Joe, Whittaker Efficient identification of independence networks using mutual information Journal Article In: Computational Statistics, vol. 28, no. 2, pp. 621-646, 2013, ISSN: 0943-4062. G, Lisboa Paulo J; H, Jarman Ian; A, Etchells Terence; J, Chambers Simon; Davide, Bacciu; Joe, Whittaker; M, Garibaldi Jon; Sandra, Ortega-Martorell; Alfredo, Vellido; O, Ellis Ian Discovering Hidden Pathways in Bioinformatics Conference Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics, vol. 7548, 2012.
@conference{Massidda2023,
title = {Causal Abstraction with Soft Interventions},
author = {Riccardo Massidda and Atticus Geiger and Thomas Icard and Davide Bacciu},
year = {2023},
date = {2023-04-17},
urldate = {2023-04-17},
booktitle = {Proceedings of the 2nd Conference on Causal Learning and Reasoning (CLeaR 2023)},
publisher = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{DBLP:journals/corr/abs-2105-06998,
title = {A causal learning framework for the analysis and interpretation of COVID-19 clinical data},
author = {Elisa Ferrari and Luna Gargani and Greta Barbieri and Lorenzo Ghiadoni and Francesco Faita and Davide Bacciu},
url = {https://arxiv.org/abs/2105.06998, Arxiv},
doi = {doi.org/10.1371/journal.pone.0268327},
year = {2022},
date = {2022-05-19},
urldate = {2022-05-19},
journal = {Plos One},
volume = {17},
number = {5},
abstract = {We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient's outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich COVID-19 dataset, showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We discuss how these computational findings are confirmed by current understanding of the COVID-19 pathogenesis. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{Bacciu2015,
title = {Probabilistic Modeling in Machine Learning},
author = {Davide Bacciu and Paulo J.G. Lisboa and Alessandro Sperduti and Thomas Villmann},
editor = {Janusz Kacprzyk and Witold Pedrycz},
url = {http://dx.doi.org/10.1007/978-3-662-43505-2_31},
doi = {10.1007/978-3-662-43505-2_31},
isbn = {978-3-662-43505-2},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
pages = {545--575},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@article{bgm2013,
title = {Efficient identification of independence networks using mutual information},
author = {Bacciu Davide and Etchells Terence A and Lisboa Paulo JG and Whittaker Joe},
url = {http://dx.doi.org/10.1007/s00180-012-0320-6},
doi = {10.1007/s00180-012-0320-6},
issn = {0943-4062},
year = {2013},
date = {2013-01-01},
journal = {Computational Statistics},
volume = {28},
number = {2},
pages = {621-646},
publisher = {Springer-Verlag},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{11568_465481,
title = {Discovering Hidden Pathways in Bioinformatics},
author = {Lisboa Paulo J G and Jarman Ian H and Etchells Terence A and Chambers Simon J and Bacciu Davide and Whittaker Joe and Garibaldi Jon M and Ortega-Martorell Sandra and Vellido Alfredo and Ellis Ian O},
doi = {10.1007/978-3-642-35686-5_5},
year = {2012},
date = {2012-01-01},
booktitle = {Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {7548},
pages = {49--60},
abstract = {The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}