Community-Based Gossip Algorithm for Distributed Averaging - Sirocchi, Christel; Bogliolo, Alessandro (Distributed Applications and Interoperable Systems, 2023)
Most real-world networks tend to organise according to an underlying modular structure, where nodes are relatively more connected with nodes belonging to the same community than others. Modularity can impact the performance of distributed data aggregation and computation in networked systems due to limited communication between communities. This paper examines the effects of modularity in the context of distributed averaging, a fundamental cooperative control problem and a central task in various applications ranging from clock synchronisation to formation control. Numerical experiments on synthetic networks demonstrate that modularity negatively affects the convergence rate of randomised gossip algorithms for distributed averaging. Further analysis suggests that nodes bridging communities (here termed boundary nodes) hold a crucial role in controlling the information flow across the network and that a modularity metric dependent on boundary nodes is a good linear predictor of performance while computable in a distributed manner. The averaging gossip protocol is then integrated with a distributed community detection algorithm, giving rise to a novel gossip scheme that leverages local community structure to improve performance by balancing the information flow at boundary nodes. The proposed community-based gossip algorithm is evaluated on synthetic modular structures, and its improved performance is confirmed by simulations run on real-world peer-to-peer networks. These findings emphasise the importance of community structure in distributed computation and might inspire further investigation in this directio

Distributed Averaging for Accuracy Prediction in Networked Systems - Sirocchi, Christel; Bogliolo, Alessandro (Modelling and Mining Networks, 2024)
Distributed averaging is among the most relevant cooperative control problems, with applications in sensor and robotic networks, distributed signal processing, data fusion, and load balancing. Gossip algorithms have been investigated and successfully deployed in multi-agent systems to perform distributed averaging in asynchronous settings. This study proposes a heuristic approach to estimate the convergence rate of averaging algorithms in a distributed manner, relying on the computation and propagation of local graph metrics while entailing simple data elaboration and small message passing. The proposed strategy enables nodes to predict the number of interactions needed to estimate the global average with the desired accuracy. Consequently, nodes can make informed decisions on their use of measured and estimated data while gaining awareness of the global structure of the network. The study presents applications to outliers identification and performance evaluation in switching topologie
Keywords: Distributed computing; Consensus; Graph metrics

Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care - Sirocchi, Christel; Suffian, Muhammad; Sabbatini, Federico; Bogliolo, Alessandro; Montagna, Sara (Proceedings of the First Workshop on Explainable Artificial Intelligence for the Medical Domain (EXPLIMED 2024) co-located with 27th European Conference on Artificial Intelligence (ECAI 2024), 2024)
In clinical practice, decision-making relies heavily on established protocols, often formalised as rules. Concurrently, machine learning (ML) models, trained on clinical data, aspire to integrate into medical decision-making processes. However, despite the growing number of ML applications, their adoption into clinical practice remains limited. Two critical concerns arise, relevant to the notions of consistency and continuity of care: (a) accuracy – the ML model, albeit more accurate, might introduce errors that would not have occurred by applying the protocol; (b) interpretability – ML models operating as black boxes might make predictions based on relationships that contradict established clinical knowledge. In this context, the literature suggests using integrated ML models to reduce errors introduced by purely data-driven approaches and improve interpretability. However, there is a lack of appropriate metrics for comparing ML models with clinical rules in addressing these challenges. Accordingly, in this article, we first propose a metric to assess the accuracy of ML models with respect to the established protocol. Secondly, we propose an approach to measure the distance of explanations provided by two rule sets, with the goal of comparing the explanation similarity between clinical rulebased systems and rules extracted from ML models. The approach is validated by employing the Pima Indians Diabetes dataset, for which a well-grounded clinical protocol is available, by training two neural networks—one exclusively on data, and the other integrating knowledge. Our findings demonstrate that the integrated ML model achieves comparable performance to that of a fully data-driven model while exhibiting superior relative accuracy with respect to the clinical protocol, ensuring enhanced continuity of care. Furthermore, we show that our integrated model provides explanations for predictions that align more closely with the clinical protocol compared to the data-driven model.<br
Keywords: Informed AI; interpretable AI; clinical protocols; diabetes

Exploring machine learning for untargeted metabolomics using molecular fingerprints - Sirocchi, Christel; Biancucci, Federica; Donati, Matteo; Bogliolo, Alessandro; Magnani, Mauro; Menotta, Michele; Montagna, Sara (Computer Methods and Programs in Biomedicine, 2024)
Background Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. Methods This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups. Results The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study. Conclusion In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning model
Keywords: Ataxia telangiectasia; Mass spectrometry; Molecular fingerprinting; Untargeted metabolomics; Machine learning

Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping - Sirocchi, Christel; Urschler, Martin; Pfeifer, Bastian (BioData Mining, 2025)
Explainable and interpretable machine learning has emerged as essential in leveraging artificial intelligence within high-stakes domains such as healthcare to ensure transparency and trustworthiness. Feature importance analysis plays a crucial role in improving model interpretability by pinpointing the most relevant input features, particularly in disease subtyping applications, aimed at stratifying patients based on a small set of signature genes and biomarkers. While clustering methods, including unsupervised random forests, have demonstrated good performance, approaches for evaluating feature contributions in an unsupervised regime are notably scarce. To address this gap, we introduce a novel methodology to enhance the interpretability of unsupervised random forests by elucidating feature contributions through the construction of feature graphs, both over the entire dataset and individual clusters, that leverage parent-child node splits within the trees. Feature selection strategies to derive effective feature combinations from these graphs are presented and extensively evaluated on synthetic and benchmark datasets against state-of-the-art methods, standing out for performance, computational efficiency, reliability, versatility and ability to provide cluster-specific insights. In a disease subtyping application, clustering kidney cancer gene expression data over a feature subset selected with our approach reveals three patient groups with different survival outcomes. Cluster-specific analysis identifies distinctive feature contributions and interactions, essential for devising targeted interventions, conducting personalised risk assessments, and enhancing our understanding of the underlying molecular complexitie
Keywords: Feature selection; Unsupervised random fores; Interpretable machine learning; Feature graphs; Disease subtyping

Federated unsupervised random forest for privacy-preserving patient stratification - Pfeifer, Bastian; Sirocchi, Christel; Bloice, Marcus D.; Kreuzthaler, Markus; Urschler, Martin (Bioinformatics, 2024)
Motivation: In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data’s role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. Results: We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. Availability and implementation: The proposed methods are available as an R-package (https://github.com/pievos101/uRF)
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Hybrid Personal Medical Digital Assistant Agents - Montagna, Sara; Sirocchi, Christel (Hybrid Personal Medical Digital Assistant Agents, 2024)
Autonomous intelligent systems are beginning to impact clinical practice as personal medical assistant agents, by leveraging experts’ knowledge when needed and exploiting the vast amount of patient data available to clinicians. However, these approaches are seldom integrated. In this paper, we propose an integrated hybrid agent architecture that combines symbolic reasoning with sub-symbolic, data-driven models. Using the PIMA dataset, we demonstrate that this hybrid approach enhances the performance of both approaches when used alone. Specifically, we show that integrating a logical agent, which uses predefined expert knowledge plans, with rules obtained by symbolic knowledge extraction from machine learning models trained on historical data, improves system reliability and clinical decision-making, while reducing misclassified instances.<br
Keywords: PMDA; Hybrid agent architecture; Symbolic knowledge extraction

Integrating Symbolic Knowledge and Machine Learning in Healthcare - Sirocchi, Christel; Montagna, Sara (Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning co-located with 20th Reasoning Web Summer School (RW 2024) as part of Declarative AI 2024, 2024)
The intersection of Artificial Intelligence and healthcare has driven advancements, particularly through machine learning, which exploits large datasets to develop predictive models and identify risk factors. Despite its success in clinical medicine, only a few models are FDA-approved due to issues of trustworthiness and lack of explainability, hindering adoption in clinical settings. Addressing these issues, symbolic knowledge injection and symbolic knowledge extraction have emerged. The first approach integrates domain-specific expertise encoded as rules into machine learning models, while the second extracts interpretable rules from trained models. In this study, this framework is validated using the Pima Indians diabetes dataset, a benchmark in diabetes research. By incorporating a diagnostic protocol for diabetes into machine learning models, the study demonstrates an improvement in the predictive capabilities of these models. By extracting rules from pure data-driven trained models and integrating them with medical knowledge, we reduce false negatives, while achieving a fully explainable diagnostic system. Finally, a combination of these two methods is explored, reporting higher diabetes detection rates and improved model explainability. Accordingly, this study demonstrates the potential of combining machine-learnt insights with medical guidelines to improve healthcare outcomes.<br
Keywords: Hybrid ML architecture; Symbolic knowledge extraction; Symbolic knowledge injection

Investigating Participation Mechanisms in EU Code Week - Sirocchi, Christel; Ostergren, Annika Pofantis; Bogliolo, Alessandro (ACM Transactions on Computing Education, 2024)
Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical, and responsible use of digital technologies in every aspect of life. DC proves essential in the contemporary digital landscape, yet its diffusion is hindered by biases, misunderstandings, and limited awareness. Teaching Informatics in the educational curriculum is increasingly supported by the institutions but faces serious challenges, such as teacher upskilling and support. In response, grassroots movements promoting computing literacy in an informal setting have grown, including EU Code Week, whose vision is to develop computing skills while promoting diversity and raising awareness of the importance of digital skills. This study extensively analyses EU Code Week editions spanning 2014 to 2021 across European Union member states, pursuing three primary objectives: firstly, to evaluate teacher engagement in the campaign in terms of penetration, retention, and spatial distribution; secondly, to characterise the multifaceted audience and themes embraced by these initiatives; and, lastly, to investigate the influence of socio-economic factors on engagement. The investigation uncovers the underlying mechanisms fostering Code Week’s engagement, providing insights to campaign organisers for strategic planning and resource allocation in future editions. Moreover, the analysis reveals that the most engaged areas are characterised by lower income as well as lower digital literacy, restricted access to technology, and a less established computer education, suggesting that Code Week thrives precisely where its impact is most neede

Large-Scale Assessment of Mobile Crowdsensed Data: A Case Study - Sirocchi, Christel; Klopfenstein, Lorenz Cuno; Bogliolo, Alessandro (IEEE Access, 2022)
Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as PPE ). The present work performs statistical modelling of PPE to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on PPE . Several road properties affecting PPE as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of PPE is evaluated, incorporating previous results on the relationship between vehicle speed and PPE . These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the publi
Keywords: Roads; Monitoring; Rough surfaces; Accelerometers; Sensors; Maintenance engineering; Indexes; Collective intelligence; large-scale assessment; mobile crowdsensing; open data

Medical-informed machine learning: integrating prior knowledge into medical decision systems - Sirocchi, Christel; Bogliolo, Alessandro; Montagna, Sara (BMC Medical Informatics and Decision Making, 2024)
Background Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML. Methods The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models. Results The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios. Conclusions By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this directio

Molecular Fingerprints-Based Machine Learning for Metabolic Profiling - Sirocchi, Christel; Biancucci, Federica; Suffian, Muhammad; Benedetti, Riccardo; Donati, Matteo; Ferretti, Stefano; Bogliolo, Alessandro; Magnani, Mauro; Menotta, Michele; Montagna, Sara (Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025)
Metabolomics has emerged as a promising field in pharmaceuticals and preventive healthcare, offering practical applications in disease detection and drug testing. However, the analysis and interpretation of complex metabolic datasets remain challenging, with current methods relying heavily on limited and incompletely annotated biological pathways. To overcome these limitations, we propose a novel approach that involves training machine learning classifiers on fingerprint-based encodings of metabolites to predict their response under specific experimental conditions. In this study, we evaluate our approach using a cellular model for the genetic disease Ataxia Telangiectasia (AT). Remarkably, some of our trained models predict affected metabolites with good performance, providing compelling evidence that the structural properties of metabolites hold predictive power over their response to specific conditions. Additionally, we suggest that evaluating the feature importance of the model can greatly assist researchers in identifying clusters of significant molecules and formulating hypotheses about affected pathways. Notably, our analysis of the AT cellular model identifies distinct groups of metabolites, some of which were already known to participate in the affected pathways, thereby validating existing knowledge. Moreover, we discovered metabolites not previously associated with AT, opening up novel opportunities for further exploratio
Keywords: Ataxia telangiectasia; mass spectrometry; metabolic pathways; untargeted metabolomics; machine learning

Neuro-Symbolic AI for Supporting Chronic Disease Diagnosis and Monitoring - Magnini, Matteo; Ciatto, Giovanni; Kuru, Ahmet Emre; Sirocchi, Christel; Montagna, Sara (2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2025)
In remote areas or regions with limited access to medical specialists, there is often a high reliance on telemedicine and Artificial Intelligence (AI)-based diagnostic tools. However, misdiagnoses or inadequate care may occur if the AI system lacks domain knowledge, failing to adhere to medical protocols. Despite the incredible research efforts applying AI in medicine, only a few models have been routinely adopted in medicine, due to issues related to trustworthiness. To address these concerns, Symbolic Knowledge Injection (SKI) has been proposed as a solution: it integrates domain-specific expertise into Machine Learning (ML) models, to improve their predictive capabilities. Despite their promising results in other fields, applicability of SKI in healthcare scenarios has not been thoroughly investigated, yet. Accordingly, in this study, we explore the applicability of a SKI method on medical datasets to evaluate: (i) how the predictive capabilities of ML models changes, (ii) their adherence to the medical protocols, and (iii) their robustness w.r.t. data degradation. Results demonstrate the potential of integrating data-driven models with established medical guidelines by improving different clinically relevant metric

Predicting metabolic responses in genetic disorders via structural representation in machine learning - Sirocchi, Christel; Biancucci, Federica; Suffian, Muhammad; Donati, Matteo; Ferretti, Stefano; Bogliolo, Alessandro; Magnani, Mauro; Menotta, Michele; Montagna, Sara (Progress in Artificial Intelligence, 2024)
Metabolomics has emerged as a promising discipline in pharmaceuticals and preventive healthcare. However, analysing large metabolomics datasets remains challenging due to limited and incompletely annotated biological pathways. To address this limitation, we recently proposed training machine learning classifiers on molecular fingerprints of metabolites to predict their responses under specific conditions and analysing feature importance to identify key chemical configurations, providing insights into the affected biological processes. This study extends our previous research by evaluating various metabolite structural representations, including Morgan fingerprint and its variants, graph-based structural encodings and proposing novel representations to improve resolution and interpretability of the state-of-the-art approaches. These structural encodings were evaluated on mass spectrometry metabolomic data for a cellular model of the genetic disease Ataxia Telangiectasia. The study found that machine learning classifiers trained on the new representations improved in classification accuracy and interpretability. Notably, models trained on graph-based encoding do not exhibit performance gains, not even with pre-training on a larger metabolite dataset, underlining the efficacy of our proposed representations. Finally, feature importance analysis across different encoding methods consistently identifies similar structures as relevant for classification, underscoring the robustness of our approach across diverse structural representation
Keywords: Ataxia telangiectasia; Mass spectrometry; Metabolic pathways; Metabolomics; Machine learning

Symbolic Knowledge Comparison: Metrics and Methodologies for Multi-Agent Systems - Sabbatini, Federico; Sirocchi, Christel; Calegari, Roberta (Proceedings of the 25th Workshop "From Objects to Agents", 2024)
In multi-agent systems, understanding the similarities and differences in agents’ knowledge is essential for effective decision-making, coordination, and knowledge sharing. Current similarity metrics like cosine similarity, Jaccard similarity, and BERTScore are often too generic for comparing knowledge bases, overlooking critical aspects such as overlapping and fragmented boundaries, and varying domain densities. This paper introduces new specific similarity metrics for comparing knowledge bases, represented via symbolic knowledge. Our method compares local explanations of individual instances, preserving computational resources and providing a comprehensive evaluation of knowledge similarity. This approach addresses the limitations of existing metrics, enhancing the functionality and efficiency of multi-agent systems.<br
Keywords: Multi-agent systems; Knowledge similarity; Symbolic knowledge

Topological network features determine convergence rate of distributed average algorithms - Sirocchi, Christel; Bogliolo, Alessandro (Scientific Reports, 2022)
Gossip algorithms are message-passing schemes designed to compute averages and other global functions over networks through asynchronous and randomised pairwise interactions. Gossip-based protocols have drawn much attention for achieving robust and fault-tolerant communication while maintaining simplicity and scalability. However, the frequent propagation of redundant information makes them inefficient and resource-intensive. Most previous works have been devoted to deriving performance bounds and developing faster algorithms tailored to specific structures. In contrast, this study focuses on characterising the effect of topological network features on performance so that faster convergence can be engineered by acting on the underlying network rather than the gossip algorithm. The numerical experiments identify the topological limiting factors, the most predictive graph metrics, and the most efficient algorithms for each graph family and for all graphs, providing guidelines for designing and maintaining resource-efficient networks. Regression analyses confirm the explanatory power of structural features and demonstrate the validity of the topological approach in performance estimation. Finally, the high predictive capabilities of local metrics and the possibility of computing them in a distributed manner and at a low computational cost inform the design and implementation of a novel distributed approach for predicting performance from the network topology.