Veřejná přednáška v rámci habilitačního řízení - Mgr. Bc. Vít Nováček, PhD
Jménem předsedy habilitační komise prof. Kozubka si Vás dovolujeme pozvat na přednášku pro odbornou veřejnost v rámci habilitačního řízení Mgr. Bc. Víta Nováčka, PhD. Přednáška proběhne v úterý 22. února 2022 ve 14 hod v posluchárně D2 v rámci Informatického kolokvia.
Název přednášky: " A Journey in Biomedical Discovery Informatics: From Ontology Learning to Knowledge Graph Embeddings"
Abstrakt: The talk will offer a crash course in biomedical discovery informatics. By and large, discovery informatics is a research discipline that addresses the information overload problem in science. More specifically, discovery informatics tries to come up with efficient ways of acquiring, integrating, organising, augmenting and utilising information and knowledge from data sets that are typically large, heterogeneous, poorly structured, fast-evolving, unreliable—or, in other words, realistic. The information overload is arguably relevant to virtually any human activity these days. However, it is particularly pertinent to life sciences. This motivates the specific instances of the information overload problem that will be addressed in the talk: 1) The vast breadth and depth of published life science articles that can hardly be utilised in a focused and exhaustive manner. 2) The largely untapped potential of networked biomedical data for making semi-automated discoveries. The presented approach to tackling the first challenge is based on advances in ontology learning from text. The other group of approaches to be introduced addresses the second challenge by means of relational machine learning, applied to link prediction in networked biomedical data. The talk will then conclude with an overview of two ongoing projects that explore clinical applications of the presented techniques.
Abstrakt: The talk will offer a crash course in biomedical discovery informatics. By and large, discovery informatics is a research discipline that addresses the information overload problem in science. More specifically, discovery informatics tries to come up with efficient ways of acquiring, integrating, organising, augmenting and utilising information and knowledge from data sets that are typically large, heterogeneous, poorly structured, fast-evolving, unreliable—or, in other words, realistic. The information overload is arguably relevant to virtually any human activity these days. However, it is particularly pertinent to life sciences. This motivates the specific instances of the information overload problem that will be addressed in the talk: 1) The vast breadth and depth of published life science articles that can hardly be utilised in a focused and exhaustive manner. 2) The largely untapped potential of networked biomedical data for making semi-automated discoveries. The presented approach to tackling the first challenge is based on advances in ontology learning from text. The other group of approaches to be introduced addresses the second challenge by means of relational machine learning, applied to link prediction in networked biomedical data. The talk will then conclude with an overview of two ongoing projects that explore clinical applications of the presented techniques.
