Prof. Tsamardinos gave an invited talk  at the DALI 2017 meeting, held in Tenerife, Canary  Islands, 18-20/4/2017. His talk focused on applying causal analysis on a complex biological problem, showing how the idiosyncrasies  of real-world tasks can drive the development of new causal algorithms and approaches.
Title:  Causal Inference from Single Cell, Mass Cytometry Data: an Integrative Approach
Abstract: Single Cell biological data, and particularly Mass Cytometry Data present significant opportunities not only for discovering novel biological data, but also for serving as a testbed of Causal Discovery algorithms. Such data seem ideal for Causal Discovery as they typically contain thousands of even millions of sample points, under several experimental conditions, and often measured at several time points. On the other hand, initial attempts at Causal Discovery demonstrate the challenges of learning causality in the microworld where confounding factors and feedback cycles are abundant. In this talk, we present approaches developed and applied on mass cytometry data that are based on Causal Probabilistic Graphical Models as well as novel approaches based on Ordinary Differential Equation Models. The results and the lesson’s learnt will be discussed, as well as their influence in inventing new methods that can robustly discover causality in single cell biological data.