Modelling and inference in leukaemia with longitudinal data: determining timing and markers plasticity within a clonal evolution model
Speaker: Prof. Giulio Caravagna
University of Trieste
DEIB - Alpha Room (Bld. 24)
December 4th, 2024 | 2.30 pm
Contacts: Marco Masseroli, Silvia Cascianelli
University of Trieste
DEIB - Alpha Room (Bld. 24)
December 4th, 2024 | 2.30 pm
Contacts: Marco Masseroli, Silvia Cascianelli
Sommario
On December 4th, 2024 at 2.30 pm the seminar titled "Modelling and inference in leukaemia with longitudinal data: determining timing and markers plasticity within a clonal evolution model" will take place at DEIB Alpha Room (Building 24).
Liquid cancers have always been one of the primal examples of the application of the clonal evolution model by Nowell. Caravagna's group studied these cancers over the last three years, focusing on their behaviour under standard treatment scenarios.
In this talk, Prof. Caravagna will overview two large applications of his computational studies on leukaemia evolution. He will concentrate on acute myeloid leukaemias (AMLs) and chronic lymphoid leukaemias (CLLs), identifying the timing of immune evasion after allogenic transplant in AMLs, and determining the plasticity of a prognostic marker for CLLs. To reach these results, his group developed advanced modelling and statistical inference frameworks, combining stochastic processes modelling from different angles, as well as Bayesian inference techniques applied to longitudinal whole-genome sequencing data.
Liquid cancers have always been one of the primal examples of the application of the clonal evolution model by Nowell. Caravagna's group studied these cancers over the last three years, focusing on their behaviour under standard treatment scenarios.
In this talk, Prof. Caravagna will overview two large applications of his computational studies on leukaemia evolution. He will concentrate on acute myeloid leukaemias (AMLs) and chronic lymphoid leukaemias (CLLs), identifying the timing of immune evasion after allogenic transplant in AMLs, and determining the plasticity of a prognostic marker for CLLs. To reach these results, his group developed advanced modelling and statistical inference frameworks, combining stochastic processes modelling from different angles, as well as Bayesian inference techniques applied to longitudinal whole-genome sequencing data.
Biografia
Giulio Caravagna is an Associate Professor in Computer Science at the Department of Mathematics, Informatics and Geosciences of the University of Trieste.
Since 2020, he has led the Cancer Data Science Laboratory, a group of interdisciplinary scientists working at the intersection of computational and experimental research, approaching real-world biological questions with state-of-the-art Machine Learning.
He trained in computer science at the University of Pisa, and after his PhD in 2011, he carried out postdoctoral training in mathematical models for biology at the Milano-Bicocca University, in machine learning for biology at the University of Edinburgh, and in evolutionary cancer genomics at the Institute of Cancer Research. His research is currently funded by a prestigious AIRC MFAG Grant (2021-26) on the application of Artificial Intelligence to cancer evolution.
Since 2020, he has led the Cancer Data Science Laboratory, a group of interdisciplinary scientists working at the intersection of computational and experimental research, approaching real-world biological questions with state-of-the-art Machine Learning.
He trained in computer science at the University of Pisa, and after his PhD in 2011, he carried out postdoctoral training in mathematical models for biology at the Milano-Bicocca University, in machine learning for biology at the University of Edinburgh, and in evolutionary cancer genomics at the Institute of Cancer Research. His research is currently funded by a prestigious AIRC MFAG Grant (2021-26) on the application of Artificial Intelligence to cancer evolution.
