Key Takeaways from Our Team’s Participation at the World Health Forum Veneto 2024

On 21-22 March 2024, our team had the pleasure of attending the Artificial Intelligence for Medicine conference, hosted within the World Health Forum Veneto in Padova.

Our PI Prof. Barbara Di Camillo delivered an insightful talk titled “Bringing AI into clinical practice: A deep dive into the BRAINTEASER project”, where she presented our work to bring responsible AI into clinical practice. On this occasion, Professor Di Camillo was also interviewed by the prestigious Corriere della Sera, as featured in the article below.

This event was also an opportunity to present some of our group’s latest work. Our senior researchers Erica Tavazzi and Enrico Longato presented a poster titled “Improving Discrimination Performance in Artificial Intelligence Models for Rare Diseases: Strategies for Dealing with Data Scarcity”, sharing insights into strategies for enhancing predictive model performance with limited data.

This event, designed to examine the current state and explore the future of medical sciences and technologies aimed at enhancing care and life quality, was a great opportunity to network and foster new collaborations.

Sysbiobig Shares AI Healthcare Research Insights at DEI-AI 2024

Yesterday we had the pleasure to contribute with 3 presentations at the research event “DEI-AI 2024” hosted by our Department of Information Engineering, where we had the opportunity to showcase our latest groundbreaking work.

In our first presentation, titled “Exploring AI Applications in Medicine: Methodological Challenges and Examples,” Barbara Di Camillo, head of Sysbiobig, delved into the intricate world of AI applications in medicine, addressing methodological challenges head-on. From robust feature selection in omics data to dynamic Bayesian networks for simulating disease progression, we showcased cutting-edge methodologies and ongoing projects, including the BRAINTEASER project.

Following that, our second talk, “Artificial Intelligence Methods to Power Real-World Clinical Studies,” was delivered by our researcher Enrico Longato. This presentation underscored the importance of real-world effectiveness in evaluating treatments. We delved into the nuances of retrospective observational studies and discussed innovative AI approaches to combine weighting and matching techniques for robust analysis, a pivotal goal we are actively pursuing within the REDDIE project.

But that’s not all! Our researcher Martina Vettoretti also presented her recently funded project titled “BREATHE – Big data, internet-of-things and aRtificial intelligence to study the impact of personal Exposure to air pollution on AsTHma Exacerbations.” This project, funded under the PRIN 2022 call, aims to revolutionise our understanding of asthma control by studying the impact of personal exposure to air pollution. By leveraging an innovative infrastructure and employing advanced machine learning techniques, Martina and her team are paving the way for the development of prognostic models capable of predicting asthma exacerbations.

These presentations truly underscore our unwavering commitment to advancing AI research in healthcare and driving real-world impact. A heartfelt thank you goes to the event organisers for providing such a platform!

Highlights from the 7th BRAINTEASER Plenary Meeting at Universidade de Lisboa

The 7th BRAINTEASER plenary meeting, held at the distinguished Universidade de Lisboa, has just concluded. The event garnered significant participation from various partner institutions, fostering lively discussions and idea exchanges. Attendees, including researchers, clinicians, and communication representatives contributed diverse perspectives, enriching the discourse. The meeting was complemented by an informal social dinner, enhancing networking opportunities.

Our team presented the upgrades and outcomes attained over the past months. We discussed the insights gleaned from examining the effectiveness of integrating pollutant data with clinical variables in Multiple Sclerosis (MS) progression predictive models. Additionally, we detailed the improvements made to both the Amyotrophic Lateral Sclerosis (ALS) and MS progression models. We explored the potential advantages of incorporating embedded stratification into our predictive models, alongside our strategy to ensure the interpretability of artificial intelligence (AI) model results. Lastly, we outlined our plan to exploit Explainable Artificial Intelligence (XAI) to identify and characterise patients for whom providing reliable predictions is challenging.

The collaborative atmosphere facilitated face-to-face meetings and stimulated engaging discussions. As the meeting drew to a close, coordinators and partners expressed optimism about BRAINTEASER’s future role in fostering collaboration and innovation within the global research community. Following two days of intensive exchanges, the consortium is poised to navigate the upcoming months with confidence.

Connecting the Dots: Exploring Air Pollution’s Role in Multiple Sclerosis Progression

In the context of the H2020 BRAINTEASER project, in collaboration with the University of Pavia and the other consortium partners, we are investigating the possible impact of air pollution on the progression of Multiple Sclerosis (MS).

Starting from the literature, where some potential correlations between pollutant agents and MS emerged, we are analysing the retrospective data provided within the project and made available to the whole research community here.
We explored the use of different machine learning techniques, including both linear and non-linear approaches, combined with the use of manual or automatic techniques for identifying the most robust features for prediction.

From our preliminary analyses on MS, the combination of dynamic environmental features with essential clinical variables appeared to be effective for enhancing the accuracy of predictive models when forecasting the occurrence of a relapse, i.e., an exacerbation of the symptoms. Noticeably, the role of environmental features was confirmed via all used feature selection approaches.

Advancing ALS Research: Collaborative Efforts with Precision ALS

Recently we had the pleasure to start collaborating with Precision ALS, a research programme for Amyotrophic Lateral Sclerosis (ALS) research across Europe, which brings together ALS clinicians, data scientists, and industries to provide new insights into the understanding of this rare disease. 

This month, we attended a Precision ALS meeting in Basel, where clinical, scientific, and industry experts gathered to discuss the latest frontiers in the development and utilisation of predictive models for ALS. Together with our partners from the University of Torino, we presented our research work in the field and the new models and tools developed within the H2020 BRAINTEASER Project.

Building upon our collective knowledge of AI applied to ALS research, we aspire to bring our expertise and insights into the collaborative initiatives of Precision ALS, advancing the understanding and treatment of this multifaceted, complex disease.

Charting the Course: Insights from AI Methodological Review for ALS Progression in the BRAINTEASER Project

In the context of the H2020 BRAINTEASER project, our group is in charge of developing predictive models for the progression of Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). In order to identify the most promising approaches to be implemented, we coordinated a systematic review of the artificial intelligence (AI) methodological landscape in ALS, focusing on patient stratification and disease progression prediction, which we performed together with the other project partners.

Out of 1604 reports, we identified 15 studies on patient stratification, 28 on ALS progression prediction, and 6 on both. We highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

The full article is available here.

These findings laid the groundwork for the development of our models within the project, providing valuable insights into the most effective AI methodologies for patient stratification and disease progression prediction in ALS. They are also guiding our direction in identifying key areas for further development and refinement.

✨ CIBB2023 Success: Advancing Computational Intelligence

The General Chairs and Local Committee want to express their heartfelt gratitude to all of you who contributed to this unforgettable and remarkable conference 🙏 From inspiring keynote lecture to innovative talk and networking opportunities, CIBB2023 has bring together brilliant researchers from Bioinformatics, Biostatistics, Systems and Synthetic Biology, and Medical Informatics 🖥️

We are proud to have contributed to the event with

  • Leveraging on somatic sample simulators for realistic data-driven generation of tumoral genomes“, Francesca Longhin, Enidia Hazizaj, Giacomo Baruzzo and Barbara Di Camillo
  • N2BPC: an algorithmic approach from Networks to Bacteria’s metabolite Production and Consumption“, Matteo Baldan, Giacomo Baruzzo and Barbara Di Camillo
  • Bactlife: A Dash GUI to simulate bacterial communities evolution“, Massimo Bellato, Marco Cappellato, Sara Rebecca, Andrea Calzavara, Alessandro Lucchiari, Niccolò Venturini Degli Esposti and Barbara Di Camillo
  • Differential cellular communication analysis from large-scale single-cell RNA sequencing data“, Giulia Cesaro, Giacomo Baruzzo and Barbara Di Camillo

Exciting moments at ISMB/ECCB2023 conference in Lyon

Our group has attended the 31st Annual Intelligent Systems For Molecular Biology (ISMB) and the 22nd Annual European Conference on Computational Biology (ECCB) conference. It was an incredibly successful event!

🚀 Our Tutorial was a triumph!

We are elated by the overwhelming response to our tutorial “How to make reproducible, portable and reusable bioinformatics software using software containerization“. The high demand showcased the significance of this topic in the software development landscape.

🎤 Sysbiobig on the Stage!

It was a privilege and an honor to present our work among esteemed professionals and researchers through 4 oral presentations and 8 poster contributions

  • Oral communication & Poster contribution “Modeling the tumor microenvironment with a hybrid Multi-Agent Spatio-Temporal model fed with sequencing data” (Mikele Milia, Giulia Cesaro, Giacomo Baruzzo, Piegiorgio Alotto, Noel Filipe da Cunha Carvalho de Miranda, Zlatko Trajanoski, Francesca Finotello, Barbara Di Camillo)
  • Oral communication “CClens: a cellular communication workflow for large-scale single-cell RNA sequencing data” (Giulia Cesaro, Giacomo Baruzzo, Barbara Di Camillo)
  • Oral communication & Poster contribution “scSeqComm: a statistical and network-based framework to infer inter- and intra-cellular communication from single-cell RNA sequencing data” (Giacomo Baruzzo, Giulia Cesaro, Barbara Di Camillo)
  • Oral communication & Poster contribution “Comprehensive benchmarking of differential abundance methods in microbiome data” (Marco Cappellato, Giacomo Baruzzo, Barbara Di Camillo)
  • Oral communication “Interactive and effective visualization framework for interpreting and exploring cellular communication data” (Giulia Cesaro, Giacomo Baruzzo, Barbara Di Camillo)
  • Poster contribution: “Bactlife – A Dash GUI to simulate bacterial communities’ evolution via agent-based modeling” (Massimo Bellato, Marco Cappellato, Sara Rebecca, Andrea Calzavara, Alessandro Lucchiari, Niccolò Venturini Degli Espositi, Barbara Di Camillo)
  • Poster contribution “Towards automatic ACMG evidence identification for variant interpretation” (Francesca Longhin, Alessandro Guazzo, Enrico Longato, Diego Boscarino, Dino Paladin, Nicola Ferro, Barbara Di Camillo)
  • Poster contribution “Comprehensive review of tumoral sample simulators: building a realistic gold standard for somatic variant calling validation” (Francesca Longhin, Enidia Hazizaj, Giacomo Baruzzo, Barbara Di Camillo)
  • Poster contribution “mopo16Sweb: A webapp for multi-objective optimization of 16S rRNA primers sequences on the cloud” (Mikele Milia, Nicola Ferro, Barbara Di Camillo, Giacomo Baruzzo)
  • Poster contribution: “Shedding light on cellular communication analysis: the present and the future” (Giulia Cesaro, James S. Nagai, Alice Chiodi, Vanessa Klöker, Nicolò Gnoato, Ettore Mosca, Ivan Costa, Enrica Calura, Barbara Di Camillo, Giacomo Baruzzo)

In two weeks we will attend the 19th Annual Meeting of the Bioinformatics Italian Society (BITS 2023) with 1 oral communication & 3 poster contributions

  • Oral communication: “CClens: effective and efficient differential cellular communication analysis of large-scale single-cell RNA sequencing data” (G. Cesaro, G. Baruzzo, B. Di Camillo)

  • Poster contribution: “Data-driven meta-simulation of realistic tumoral samples” (F. Longhin, E. Hazizaj, G. Baruzzo, B. Di Camillo)
  • Poster contribution: “mopo16Sweb: a cloud-based app for multi-objective optimization of bacterial 16S PCR primers” (M. Milia, N. Ferro, B. Di Camillo, G. Baruzzo)
  • Poster contribution: “From microbial ground truth network simulation to inference method benchmark” (M. Baldan, A. Rossato, M. Bellato, G. Baruzzo, B. Di Camillo)

For more details, check the conference website: