Discussion Panel | Wei4H 2021

00:00 - 17:22 Ηθική, Καινοτομία, Βιώσιμη Ανάπτυξη - Δημήτρης Καλογερόπουλος

17:23 - 55:14 Robotics, AI, and Data Science towards Healthcare and Wellbeing in and after the Pandemic - Νικόλαος Μαυρίδης

By its definition, Wellbeing encompasses a wider spectrum then classical Healthcare, and furthermore, is not bound to have either intact or negative values, but rather can arguably reach increasingly higher levels. The recent pandemic had created an unprecedented push in the adoption of numerous technologies of the fourth industrial revolution, many of them either focusing on or affecting healthcare and wellbeing. In this talk, after an introduction to relevant themes and concepts, an anthology with three parts will be provided: First, “Pandemic Cohort Management”, a Data-Science-Driven event-level risk management Intel – SAP collaboration project addressing the pandemic will be briefly presented; Second, examples of enterprise-level data science usecases for healthcare will be given, and third, several applications of robotics in healthcare and wellbeing will be introduced.

55:15 - 57:04 Δημήτρης Καλογερόπουλος, Νικόλαος Μαυρίδης, Σαββίδου Φωτεινή

57:05 - 1:24:16 AI & IoT for eHealth & Clinical Trials - Θάνος Σταυρόπουλος

1:24:17 - 1:25:20 Σαββίδου Φωτεινή, Θάνος Σταυρόπουλος, Δημήτρης Καλογερόπουλος

1:25:21 - 1:52:27 Multi-omics pathway tools in drug discovery: Applications for better efficacy and toxicity - Λεωνίδας Αλεξόπουλος

A major challenge for bringing safe and effective new treatment to patients is the deep understanding of a disease. Here, we describe multi-omic technologies and systems biology algorithms for tackling major questions in the drug discovery and development pipeline: (i) construction of pathways and comparison between normal or diseased cells, (ii) identification of drug mode of action (MoA), and (iii) prediction of drug toxicity and efficacy. Our network based approach is able to predict the drugs’ main target and uncover off-target effects. Subsequently, machine learning algorithms can select MoAs with reduced toxicity, increased efficacy and tailor drugs to specific disease mechanisms. So far, we have applied our approach in liver cancer, osteoarthritis, multiple sclerosis, non-alcoholic fat liver disease, chronic kidney disease and more recently in melanoma. Our pathway analysis algorithms and high throughput multiplex platform pave the road for new solutions in early drug discovery.

1:52:28 - 1:54:28 Σαββίδου Φωτεινή, Λεωνίδας Αλεξόπουλος, Δημήτρης Καλογερόπουλος

1:54:29 - 2:12:59 Σαββίδου Φωτεινή, Λεωνίδας Αλεξόπουλος, Δημήτρης Καλογερόπουλος, Νικόλαος Μαυρίδης, Θάνος Σταυρόπουλος

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