Alessandro Cestaro (Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, CNR, Bari). I obtained my MSc in Biology at Padua University in 2002 working in the Biophysics field; during this work, I began to explore the applications of Computer Science in Biology. Going further in that direction I gained my PhD in 2005, still at Padua University, with a thesis about genome sequencing and annotation of deep sea bacterium P. profundum. Between 2006 and 2022 I worked at Fondazione Edmund Mach as a bioinformatician, specializing in genome data analysis and genome data management. In 2023 I started as Node Coordinator of ELIXIR Italy (https://elixir-italy.org) and Research Infrastructure manager for the CNR project ELIXIRxNextGenIT.
Talk title : ELIXIRxNextGenIT, consolidating the ELIXIR Italian Node.
Abstract: Under the framework of NextGenerationEU recovery plan, the ELIXIRxNextGenerationIT project is dedicated to fortifying the Italian node of ELIXIR-IT. This initiative aims to enrich existing services and integrate novel offerings into a cohesive Service Delivery Plan (SDP). ELIXIRxNextGenIT represents a significant opportunity for the Italian node, facilitating streamlined coordination of its activities across a distributed infrastructure. By optimizing orchestration, these endeavors will enhance the node’s service portfolio, ensuring a harmonized presentation of services. This consolidation not only strengthens the impact of ELIXIR-IT within Italy but also amplifies its significance on the global stage, enriching its utility for both national and international stakeholders.
Simon Hodson (CODATA, International Science Council, Paris). Executive Director of CODATA since August 2013, Simon Hodson is an expert on data policy issues and research data management. He chaired the European Commission’s Expert Group on FAIR Data which produced the report Turning FAIR into Reality, and he was vice-chair of the UNESCO Open Science Advisory Committee, tasked with drafting the UNESCO Recommendation on Open Science. Simon is also a member of the Data Documentation Initiative Scientific Board, a member of the EOSC Semantic Interoperability Task Force and the coordinator of the WorldFAIR Project.
Talk title: FAIR to Enable Cross-Domain Research
Abstract: The major global scientific and human challenges of the 21st century, encompassed by the Sustainable Development Goals, can only be addressed through cross-domain research that seeks to understand complex systems through machine-assisted analysis at scale. Our capacity for such analysis is currently constrained by the limitations in our ability to access and combine heterogenous data within and across domains. The FAIR principles and the frameworks set by Open Science provide a significant part of the solution. Attention needs to be paid to the interfaces where data is used across disciplines: the biological sciences have a vital role to play in this work. To address these issues, CODATA has been entrusted by the International Science Council (ISC) to develop a programme of activity: ‘Making Data Work for Cross-Domain Grand Challenges’. The flagship is the WorldFAIR project which focuses on the implementation of the FAIR principles both within and across 11 different domain and cross-domain case studies, with a central effort to understand and guide cross-domain FAIR. WorldFAIR provides guidance for FAIR implementation both within specific domains and infrastructures and across them. This presentation will outline a number of concrete examples of work to advance cross-domain interoperability of relevance to the biological sciences data community, including the Cross-Domain Interoperability Framework (CDIF) which identifies a set of functional requirements for interoperability, particularly for steps in data combination, and recommends good practices for each of these requirements, in relation to the use of existing or emerging standards and specifications.
Giovanni Iacca ( Department of Information Engineering and Computer Science, University of Trento). Giovanni Iacca is an Associate Professor in Information Engineering at the Department of Information Engineering and Computer Science of the University of Trento, Italy, where he founded the Distributed Intelligence and Optimization Lab (DIOL). Previously, he worked as a postdoctoral researcher in Germany (RWTH Aachen, 2017-2018), Switzerland (University of Lausanne and EPFL, 2013-2016), and The Netherlands (INCAS3, 2012-2016), as well as in industry in the areas of software engineering and industrial automation. He is co-PI of the PATHFINDER-CHALLENGE project "SUSTAIN" (2022-2026). Previously, he was co-PI of the FET-Open project "PHOENIX" (2015-2019). He has received two best paper awards (EvoApps 2017 and UKCI 2012). His research focuses on computational intelligence, distributed systems, explainable AI, and analysis of biomedical data. In these fields, he co-authored more than 150 peer-reviewed publications. He is actively involved in organizing tracks and workshops at some of the top conferences on computational intelligence, and he regularly serves as a reviewer for several journals and conference committees. He is an Associate Editor for IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and Frontiers in Robotics and AI.
Talk title : When Explainable AI and Evolutionary Computation meet.
Abstract: Motivated by the need for explanations in safety-critical applications, the field of Explainable Artificial Intelligence (XAI) has recently attracted a great interest in the AI community. Interestingly, some of the advances in XAI are based on Evolutionary Computation (EC). For instance, Genetic Programming has been extensively used to induce various kinds of white-box models, such as decision trees or rule-based systems. On the other hand, also within the EC field there is now a growing concern about explainability, since one may often need to explain how a population-based method conducted its search process and reached a certain outcome. In this talk, first I will give a general overview on the connection between EC and XAI. Then, I will highlight some recent works where EC has been used, also in connection with Reinforcement Learning, to create systems capable of solving in an interpretable way a variety of tasks, e.g., in medical imaging and pandemic control. Finally, I will discuss what I believe are the most interesting challenges and opportunities that lie at the intersection of the two fields.
Giuseppe Jurman (Fondazione Bruno Kessler). Giuseppe Jurman is a mathematician with a PhD in Algebra, currently Head of the Data Science for Health (DSH) Unit at FBK. His main interest is the development and application of artificial intelligence, machine learning and complex network models for diagnosis, prognosis and prediction in medicine, life science and computational biology, starting from EHRs, omics data and biomedical images, including digital pathology, with a particular emphasis on reproducibility and explainability. He is also interested in scientific programming with Python and other computing languages, and he teaches Data Visualization at the M.Sc. in Data Science at the University of Trento.
Talk title : Generative AI in sequencing: enhancing models by synthetic omics data.
Abstract: Synthetic data have recently gained momentum in several scientific areas as an effective solution to deal with several aspects of data poverty and missingness. In translational medicine, biomedical images and EHR data have been the first to benefit from synthetic augmentation techniques through generative AI algorithms such as GANs or, more recently, Diffusion-like models. Extension of these methods to omics data poses further challenges due to the nature of the signal, such as the need of taking into account sample variability and multilevel omics coherence. In this talk, we will present an overview of the state-of-the-art of the synthetic data in the omics universe, including the generative methodologies, the future perspectives, and the related caveats, concluding with some applicative use cases (joint work with Marco Chierici and Silvia Menchetti).
Marco Moretto (Fondazione Edmund Mach, San Michele all’Adige). Marco Moretto is a technologist at the Fondazione Edmund Mach in San Michele all’Adige, Trento, Italy. He is a bioinformatician with a background in Computer Science. He completed his PhD in Padua, supervised by Chiara Romualdi and Kristof Engelen, focusing on the integration and analysis of transcriptomic data. Throughout his career, he has collaborated on various transcriptomic data analysis projects, as well as genome assembly projects, including those for grapevine, apple, and, more recently, lemon. Currently working under the supervision of Pietro Franceschi, he is engaged in digital agriculture data analysis. He is also a member of the COST Integrative Grant IG17111 Grapedia and serves as the Local Task Manager at FEM for PNRR Agritech Task 1.2.2.
Talk title: The Grapedia Initiative: Building the Grapevine Genomic Encyclopedia.
Abstract: Genomic tools and databases are essential in advancing crop improvement and biotechnology. The GRAPEDIA project addresses the need for grapevine-specific resources by creating a centralized global database. This initiative integrates gene catalogs, genome sequences, annotations, and omics data, enhancing the understanding of grapevine genetics. It facilitates collaboration and data sharing among researchers and industry professionals, providing a platform with significant economic potential. GRAPEDIA’s multi-layered architecture is centered around a No-SQL database, exposing a unified data model via a GraphQL interface, supporting bioinformatic workflows and interactive dashboards for gene-centric analysis. The web portal, built with ReactJS and NodeJS, acts as a hub for information consolidation, offering a consistent user experience and secure data management. It enables the execution of tools, visualization of dynamic dashboards, and provides workflows for download or cloud execution. Committed to transparency, GRAPEDIA offers comprehensive documentation and best practices for scientific reproducibility, with all code being open-source to encourage community contributions. This community-driven project simplifies grapevine genomic research, promoting a unified approach to data analysis and application.
Eva Maria Novoa (Center for Genomic Regulation, Barcelona). Eva Maria obtained her BSc in Biochemistry in 2007 and PhD in Biomedicine in 2012 in Barcelona (Spain). Since 2018, she is Group Leader of the “Epitranscriptomics and RNA Dynamics” laboratory at the Center for Genomic Regulation (CRG) in Barcelona, Spain. Her laboratory is focused on deciphering the language of RNA modifications, and how its orchestration can regulate our cells in a space-, time- and signal-dependent manner. Specifically, her laboratory has put significant efforts in the development of novel algorithms to map and quantify RNA modifications using nanopore sequencing, as well as optimized the library preparations to make the technology applicable to low-input samples and beyond mRNAs.
Talk title: Decoding the mRNA and tRNA epitranscriptome at single molecule resolution.
Abstract The dynamic deposition of chemical modifications into RNA is a crucial regulator of temporal and spatial accurate gene expression programs. A major difficulty in studying these modifications, however, is the need of tailored protocols to map each RNA modification individually. In this context, direct RNA nanopore sequencing (DRS) has emerged as a promising technology that can overcome these limitations, as it is in principle capable of mapping all RNA modifications simultaneously, in a quantitative manner, and in full-length native RNA reads. Here I will present the latest work on how we can use DRS to identify RNA modifications with single nucleotide and single molecule resolution, how we can adapt DRS to efficiently capture small RNAs such as tRNAs, to then study the biological functions and dynamics of the epitranscriptome and its interplay with other regulatory layers, with the final goal of deciphering why and how epitranscriptomic dysregulation is connected to human disease.
Silvia Parolo (Fondazione COSBI). Silvia Parolo is a computational biologist with expertise in omics data analysis and systems biology. She is currently the Head of Systems Biology at Fondazione COSBI. After receiving her Bachelor’s and Master’s degrees in molecular biology, in 2014 she obtained her Ph.D. from the University of Pavia, working on genome-wide approaches applied to genomic data. Since joining Fondazione COSBI in 2015, Silvia has had the opportunity to work on various projects through academic and industrial collaborations. Her research focuses on developing integrative approaches to study biological processes and identify candidate therapeutic targets.
Talk title: Exploring New Therapeutic Opportunities for Common and Rare Disorders through Network-Based Drug Repurposing.
Abstract: Drug repurposing seeks to find new indications for existing drugs, aiming to shorten the drug development pipeline, characterized by extremely high costs and failure rates. Indeed, the use of approved drugs helps reduce the time and costs associated with the evaluation of drug safety and tolerability. While in the past drug repurposing was mainly the result of serendipitous observations, computational drug repurposing applies systematic data-driven approaches. Among the available computational methods, network analysis offers the possibility of integrating heterogeneous data types, from omics datasets to biomedical knowledge present in pathways and ontologies and drug information, such as targets and transcriptional signatures. In this talk, I will present our approach to network-based drug repurposing, showcasing examples applied to both complex multifactorial disorders and rare Mendelian diseases. I will show how tissue-specific networks allowed us to point out repurposing candidates for metabolic syndrome, how we inferred an Alzheimer’s disease cell-cell network from single-cell data for drug repurposing goals, and the challenge of finding drug repurposing candidates for the rare disease cystinosis.
Johannes Rainer (Institute for Biomedicine, Eurac Research, Bolzano). Johannes Rainer is principal investigator for computational mass spectrometry and metabolomics at the Institute for Biomedicine of Eurac Research, Bolzano, Italy. Next to the analysis and integration of large-scale metabolomics and proteomics data sets, he and his team are developing and contributing to a large set of R/Bioconductor software packages for mass spectrometry data analysis. Dr Rainer is also co-founder of the RforMassSpectrometry initiative and a member of the Bioconductor Community Advisory Board.
Talk title: An Open Software Development-based Infrastructure for Efficient Mass Spectrometry Data Analysis.
Abstract: A frequent problem with scientific research software is the lack of support, maintenance, and continued enhancement. In particular, development lead by a single researcher can easily result in orphaned software packages, especially if combined with poor documentation or lack of adherence to open software development standards. The RforMassSpectrometry initiative aims to establish an efficient and stable infrastructure for mass spectrometry (MS) data analysis. To this end, a growing ecosystem of R software packages is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems, community contributions are fostered, while open development, documentation and long-term support are emphasized. At the heart of the package ecosystem is the Spectra package which provides the core infrastructure to handle and analyze MS data. Its design allows easy expansion to support additional file or data formats including data representations with minimal memory footprint or remote data access. Through chunk-wise data processing and a lazy processing queue analysis of also very large data sets is supported. All packages are available through the Bioconductor project and enable the creation of customized, data set specific, and reproducible analysis workflows.
Levi Waldron (CUNY Graduate School of Public Health and Health Policy, New York). Levi Waldron is active in public health data science, combining statistics and computation to address data-intensive public health problems, particularly in the field of translational cancer research. He is a technical advisor to the Bioconductor project for computational biology and the author of multiple software and data packages for genomic analysis and analysis of the human microbiome. Prior to his work at CUNY ISPH, Dr. Waldron completed a post-doctoral fellowship at the University Health Network in Toronto, and at the Harvard School of Public Health and Dana Farber Cancer Institute. Dr. Waldron’s implementation science expertise focuses on assessing the effectiveness of novel HIV patient care, and the clinical translation of molecular subtypes of cancer.
Talk title: Curating single cell multimodal landmark datasets for R/Bioconductor
Preparata Lecture
Nicola Segata (Department CIBIO, University of Trento and European Institute of Oncology, Milan). Nicola Segata is Professor and Principal Investigator at the CIBIO Department of the University of Trento (Italy) and Principal Investigator at the European Institute of Oncology in Milan (Italy). His lab (http://segatalab.cibio.unitn.it/) comprises employs experimental metagenomic tools and novel computational approaches to study the diversity of the microbiome across conditions and populations and its role in human diseases. The projects in the lab bring together computer scientists, microbiologists, statisticians, and clinicians and are generally focused on profiling microbiomes with strain-level resolution and on the meta-analysis of very large sets of metagenomes with novel computational tools.
Talk title: Computational metagenomics to model natural and therapeutic human microbiome transmission.
Abstract: The human microbiome is an integral component of the human body, but while extensive research explores its impact on health, a key question remains unanswered: how person-to-person interactions influence the personal makeup and spread of these microbial communities within and across populations. I will present how novel computational tools, applied to a massive integrated dataset of over 20,000 metagenomes, reveal extensive bacterial strain sharing (over 20 million instances) across individuals. These findings unveil distinct patterns of transmission, including those involving mother-to-infant, infant-to-infant, food-to-humans, intra-household, and even pet-to-infant microbial interactions. Additionally, the framework sheds light on the factors influencing success rates in fecal microbiota transplantation, paving the way for improved clinical outcomes. The sheer volume of microorganism transmission I will describe underscores its profound relevance for both human microbiome research and clinical applications.
