125 citations · 226 across the 9 of their papers we have counts for
9 papers
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
Ruben Weitzman, Peter Mørch Groth, Lood Van Niekerk +4
Retrieving homologous protein sequences is essential for a broad range of protein modeling tasks such as fitness prediction, protein design, structure modeling, and protein-protein…
Multi-megabase scale genome interpretation with genetic language models
Frederik Träuble, Lachlan Stuart, Andreas Georgiou +9
Understanding how molecular changes caused by genetic variation drive disease risk is crucial for deciphering disease mechanisms. However, interpreting genome sequences is challeng…
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
Clare Lyle, Arash Mehrjou, Pascal Notin +4
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the…
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani +15
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. Such maps are not only foundational for understanding the molecular mechanism…
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer +4
The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease li…
RITA: a Study on Scaling Up Generative Protein Sequence Models
Daniel Hesslow, Niccoló Zanichelli, Pascal Notin +2
In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences…
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson +4
In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that s…
Improving black-box optimization in VAE latent space using decoder uncertainty
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
Optimization in the latent space of variational autoencoders is a promising approach to generate high-dimensional discrete objects that maximize an expensive black-box property (e.…
Improving compute efficacy frontiers with SliceOut
Pascal Notin, Aidan N. Gomez, Joanna Yoo +1
Pushing forward the compute efficacy frontier in deep learning is critical for tasks that require frequent model re-training or workloads that entail training a large number of mod…