This is a hybrid event. Join in person at the LCSB, or tune in remotely via Webex.
Biological Networks: Building Connections for a Broader Perspective
The era of big data in biomedical sciences presents a valuable opportunity to comprehend the vast heterogeneity of genetic and environmental factors influencing biological regulation and observed phenotypes.
With the expanding data volume and knowledge of molecular mechanisms underlying disease onset and progression, medicine’s focus has transitioned from treatment to prevention. Precision medicine relies on classifying patients into distinct molecular subgroups to ensure targeted treatments based on precise diagnoses. Defining these subgroups requires a deep understanding of biological mechanisms, genotype-phenotype relationships, and the influence of environmental and individual factors.
The advancement of high-throughput technologies has led to an exponential increase in data availability at reduced costs, laying the foundation for a holistic approach to biomedical research. Data from diverse molecular sources can be integrated and analyzed as an interconnected system of interactions and dependencies. Computational and mathematical modelling facilitate this integration, forming the basis of systems and network biology.
Networks provide a framework for studying the properties of complex systems that arise from interactions between individual components. In this talk, we will explore several topics in network biology, including biological network inference and comparison, multimodal data integration, machine learning applications in network analysis, and network-based approaches to precision medicine.
About the speaker
Dr Ilaria Granata is a researcher in the Computational Data Science group at the Institute for High-Performance Computing and Networking of the National Research Council of Italy in Naples. She previously held a research position at BioNat Italia and received her PhD from the University of Salerno, Italy.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
