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Found 10 papers, 6 papers with code
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
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 interactions.
Multi-megabase scale genome interpretation with genetic language models
Here, we present Phenformer, a multi-scale genetic language model that learns to generate mechanistic hypotheses as to how differences in genome sequence lead to disease-relevant changes in expression across cell types and tissues directly from DNA sequences of up to 88 million base pairs.
Multi-Scale Representation Learning for Protein Fitness Prediction
Designing novel functional proteins crucially depends on accurately modeling their fitness landscape.
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms.
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
Notably, the winning solutions significantly improved performance compared to previous baselines, establishing a new state of the art for this critical task in biology and medicine.
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
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 likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins.
RITA: a Study on Scaling Up Generative Protein Sequence Models
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 belonging to the UniRef-100 database.
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.
Improving black-box optimization in VAE latent space using decoder uncertainty
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. g., drug-likeness in molecular generation, function approximation with arithmetic expressions).
Improving compute efficacy frontiers with SliceOut
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 models.
