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. Your personal join link will be valid for all upcoming lectures of the series.

Register

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: Human variant interpretation with sequence-to-activity models

4 June 2025 5:30 p.m. - 6:30 p.m. Central European Time

Speaker: Katherine Pollard, Gladstone Institute of Data Science & Biotechnology, University of California San Francisco, Chan Zuckerberg Biohub

Abstract:

The role of computational science in biomedical research has typically been downstream of experiments, where it plays important roles in signal processing, data integration, pattern detection, and hypothesis testing. But this is changing, and predictive models are now being used to generate and test hypotheses in silico. This change has been fueled by experimental technologies that drive rapid growth of biomedical databases couple with breakthroughs in computer hardware and software that have enabled the development of highly accurate, predictive modeling frameworks. My lab leverages these opportunities to decode how the human genome works, evolves, and breaks in disease. In this talk, I will share examples from human genomics, where we have built deep learning models to predict molecular phenotypes, such as epigenetic states, genome folding, and gene expression from DNA sequence alone. Then, we use the models to computationally predict how changing the DNA sequence alters genome function. This strategy leads to causal hypotheses and enables us to prioritize disease variants with predicted functional effects. Experiments designed using model outputs are accelerating the rate of discoveries, shedding light on genetic mechanisms in human evolution and disease. This prediction-first strategy exemplifies my vision for a more proactive, rather than reactive, role for computational science in biomedical research.

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