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About the speaker
Dr. Jacob Schreiber is a Guest Scientist at the Institute of Molecular Pathology in Vienna and an incoming Assistant Professor at the University of Massachusetts Chan Medical School. His research involves the application of machine learning to understand the regulatory role of each nucleotide in the genome and how this changes across every cell in our bodies, and the development of an open-source ecosystem tools to make doing so easy for everyone.
Abstract
Sequence-based machine learning (ML) models have proven invaluable for studying regulatory genomics, with two key application areas being identifying the sequence features driving biochemical activity and designing novel sequences that exhibit desired characteristics. However, these seemingly different tasks are just two sides of the same coin, with the algorithms for each being remarkably similar. To demonstrate this, I will first introduce DragoNNFruit, a dynamic neural network for identifying and extracting the continuous regulatory drivers of chromatin accessibility at single-cell resolution. Then, I will introduce Ledidi, which turns any already-trained ML method into a designer of genomic sequences with desired characteristics.
About the seminar series
Once a month, NCMBM invites international guest speakers to present on topics within molecular life science and medicine.
The seminar is open to all, with no registration needed.
Tea, coffee and a simple lunch will be served at 11.30 before the seminar.