09 Feb In-person UBC Seminar (16:00Mar4)@ANDRO C.137, UU. “Bioinformatics paves the way for understanding the developmental competence of the bovine conceptus”
On Monday, March 4 at 16:00, Dr. Belen Rabaglino from the Faculty of Veterinary Medicine, UU will share her most recent bioinformatics related research.
Just come without registration. Don’t miss this opportunity to learn more about the work of one of our new PIs and to catch up with other UBCers with drinks!
Bioinformatics paves the way for understanding the developmental competence of the bovine conceptus
In cattle, pregnancy loss due to embryonic and foetal mortality is a major concern that significantly impacts reproductive efficiency. Given the economic importance of cattle in livestock productivity, much research is being carried out to comprehend the regulatory mechanisms underlying pregnancy failures. Indeed, unravelling the molecular principles of the developing conceptus is paramount. Numerous investigators, including myself, have applied RNA sequencing to study the bovine embryonic and foetal transcriptome at different developmental checkpoints and under various conditions. In this seminar, I will overview transcriptomic studies performed at critical stages of development to characterize the molecular landscape of the bovine conceptus that defines its ability to establish pregnancy. Furthermore, I will demonstrate how machine learning models are powerful tools that can help us understand the underlying biology driving embryonic and foetal competence for survival.
There will be a 35-min presentation followed by a 15-min in-depth discussion.
Speaker overview
Dr. Belen Rabaglino is an assistant professor at the Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University (UU). She is a veterinary scientist involved in Animal Reproduction. Her research has focused on applying transcriptomics and epigenomics to understand endometrial biology and embryonic and fetal development in ruminants. Her philosophy for analysing and interpreting this data type is to use traditional and innovative approaches, such as predictive models based on machine learning algorithms, and adapt these methods to generate meaningful results in the biological context of the experiment.
Location: Androclus Building C.137, Yalelaan 1, 3584 CL.
Please join our UBC Slack workspace and we will keep you posted: click here.