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.


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 Nucleotide Transformer initiative: building and evaluating robust foundation models for genomics
8 May 2024 5:30 p.m. - 6:30 p.m. Central European Time

Speaker: Thomas Pierrot, InstaDeep


The human genome sequence provides the underlying code for human biology. Since the sequencing of the human genome 20 years ago, a main challenge in genomics has been the prediction of molecular phenotypes from DNA sequences alone. Models that can “read” the genome of each individual and predict the different regulatory layers and cellular processes hold the promise to better understand, prevent and treat diseases. Here, we introduce the Nucleotide Transformer (NT), our initiative to build robust and general DNA foundation models that learn the languages of genomic sequences and molecular phenotypes. We will first present our first collection of DNA foundational models, having up-to 2.5B parameters and being pre-trained on 850 genomes from various species. The Nucleotide Transformer (NT) models v1 and v2 and agroNT, a version specific for agricultural applications, have learned transferable, context-specific representations of nucleotide sequences, and can be fine-tuned at low cost to solve a variety of genomics applications. We will then discuss avenues on how to improve these models to tackle modern challenges in the field. Notably, we will use this discussion as an opportunity to present our progress on several fronts towards more general genomics AI agents that integrate different modalities and have improved transfer capabilities. The training and application of such foundational models in genomics provide a widely applicable stepping stone to bridge the gap of accurate predictions from DNA sequence.

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