Kipoi - Seminar

The monthly virtual seminar series is designed as a platform for interested Kipoi users and developers and will host talks on the applications of deep learning on biological data. The seminar is held on every first Wednesday of the month at 5:30 p.m. - 6:30 p.m. CET. We are also happy to share the recordings of the seminar on YouTube.

How to take part

The Virtual Seminar Series takes place via Zoom. To take part in the seminar, you can register for the online Zoom conference here. Your personal join link will be valid for all upcoming lectures of the series.

How to apply as a speaker

The seminar is a great opportunity to present your recent work to a large international audience. If you want to apply as a speaker, please use the contact in the registration confirmation email.

Next seminar

Title: The 3D genome and predictive models of gene regulation

4 August 2021 5:30 p.m. - 6:30 p.m. Central European Summer Time

Speaker: Christina Leslie, Sloan Kettering Institute, New York

Abstract:

Recent years have seen rapid strides in 3D chromosomal conformation capture technologies, with the development of improved Hi-C protocols and techniques like HiChIP to enrich for specific types of 3D interactions, all enabling the generation of high resolution interaction data sets. However, the computational analysis of these data presents many challenges, and resolving how 3D genomic architecture influences gene regulation remains an important open problem. We will present recent work in the lab to improve the computational analysis of 3D interaction data and to incorporate 3D genomic architecture into predictive models of gene regulation using deep learning. We will briefly describe HiC-DC+, our new computational tool for identifying significant and differential 3D interactions from Hi-C and HiChIP data sets, and we will show how more rigorous statistical analysis empowers biological interpretation. We will then present a graph attention network framework to incorporate 3D interactions in predictive models of gene regulation, using 1D epigenomic data with or without genomic DNA sequence together with 3D connectivity to predict gene expression. We use feature attribution on these models to infer functional enhancer-promoter interactions, outperforming the state-of-the-art approach to this problem.

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