Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level. The amount of publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing 50M records for humans with each record measuring 20,000 genes.

We help address these challenges through three contributions. First, we publish a new dataset, EHRSHOT, which contains de-identified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine.

Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design.

Table 3: Comparison of models trained with different representations of protein structure across various tasks, on a random data split . The optimal choice of representation depends on the task. Shown are mean and standard deviation across four runs with different seeds. Table 4: Comparison of models trained with different representations of protein structure across various tasks, on a sequence data split . Table 5: Comparison of models trained with different representations of protein structure across various tasks, on a structure data split .

However, the heterogeneity of protein data constitutes a problem for reproducible machine learning.

The structure of protein-protein complexes is critical for understanding binding dynamics, biological mechanisms, and intervention strategies.

Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery.

CIFAR-10) and tested on another (e.g.

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.

Each row of an MSA is a protein sequence.