Generative AI in Genomics (Gen²): Barriers and Frontiers

Sarah Teichmann (University of Cambridge)

Keynote: Mapping the Human Body One Cell at a Time

Abstract: The 37 trillion cells of the human body have a remarkable array of specialised functions and must cooperate in time and space to construct a functioning human. Combining single cell and spatial genomics with AI/ML tools and data science, my lab has been attempting to understand this cellular diversity, how it is generated during development and how it goes wrong in disease. My talk will present recent advances towards a virtual Human Cell Atlas (HCA). I will highlight methods we developed to integrate and annotate cell atlases, and discuss how these models can be used to disentangle the effect of covariates, predict unseen perturbations and decode the regulatory logic of human cell types. I will additionally describe GenAI-based approaches we developed to model the spatial organization of cell types within organs, which can be leveraged to predict the effect of a cell on its neighbours.

Maria Brbic (EPFL)

TBD

Abstract: TBD

Nic Fishman (Harvard University)

Generative Distribution Embeddings

Abstract: Many real-world problems require reasoning across multiple scales, demanding models which operate not on single data points, but on entire distributions. We introduce generative distribution embeddings (GDE), a framework that lifts autoencoders to the space of distributions. In GDEs, an encoder acts on sets of samples, and the decoder is replaced by a generator which aims to match the input distribution. This framework enables learning representations of distributions by coupling conditional generative models with encoder networks which satisfy a criterion we call distributional invariance. We show that GDEs learn predictive sufficient statistics embedded in the Wasserstein space, such that latent GDE distances approximately recover the W2 distance, and latent interpolation approximately recovers optimal transport trajectories for Gaussian and Gaussian mixture distributions. We systematically benchmark GDEs against existing approaches on synthetic datasets, demonstrating consistently stronger performance. We then apply GDEs to six key problems in computational biology: learning representations of cell populations from lineage-tracing data (150K cells), predicting perturbation effects on single-cell transcriptomes (1M cells), predicting perturbation effects on cellular phenotypes (20M single-cell images), modeling tissue-specific DNA methylation patterns (253M sequences), designing synthetic yeast promoters (34M sequences), and spatiotemporal modeling of viral protein sequences (1M sequences).

Contributed Talks

Selected work from submissions

~3 talks selected from submitted papers. TBD.

Gokcen Eraslan (Genentech)

TBD

Abstract: TBD

Adam Kosiorek (Google Deep Mind)

TBD

Abstract: TBD

Panel Discussion

Panelists:
TBD