285 citations · 298 across the 6 of their papers we have counts for
6 papers
RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy
Oren Kraus, Federico Comitani, John Urbanik +6
High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of…
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik +10
Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To us…
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian +9
High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the…
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the…
METCC: METric learning for Confounder Control Making distance matter in high dimensional biological analysis
Kabir Manghnani, Adam Drake, Nathan Wan +1
High-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects. These effects include both technical e…
Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU
Imran S. Haque, Vijay S. Pande
Graphics processing units (GPUs) are gaining widespread use in computational chemistry and other scientific simulation contexts because of their huge performance advantages relativ…