Predicting activity patterns of regulatory elements in zebrafish using ML
The talk is available online too, see booking url for Teams link
During eukaryotic cell division, the genome folds into a three-dimensional structure that defines how regulatory elements, such as enhancers, interact. Furthermore, studies demonstrate that some folding (or mis-folding) patterns of the genome are closely associated to diverse diseases, such as cancer. Therefore, the identification of genome folding patterns has become a highly interesting topic for scientists to interpret how gene regulation is influenced by chromatin architecture formation. However, chromatin folding presents different patterns depending on the cell type and developmental stage of the organism. Here, we introduce three predicting models based on DeepC, an algorithm that uses transfer learning followed by CNNs, able to predict chromatin interactions in short- and long-distance scales from Hi-C datasets of brain, muscle of adult and whole-embryo zebrafish using transcriptional data alone.
Date: 15 July 2022, 16:00 (Friday, 12th week, Trinity 2022)
Venue: Wolfson College, Linton Road OX2 6UD
Venue Details: Private Dining Room
Speaker: Andrea Rodriguez Delherbe (University of Oxford)
Organising department: Wolfson College
Organiser: Yaling Hsiao (University of Oxford )
Organiser contact email address:
Part of: Machine Learning Research Cluster Seminar Series at Wolfson College
Booking required?: Not required
Booking url:
Cost: Free. Tea and coffee provided too.
Audience: Public
Editor: Yaling Hsiao