2.4k citations · 4k across the 15 of their papers we have counts for
15 papers
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA
Aman Patel, Arpita Singhal, Austin Wang +3
Recent advances in self-supervised models for natural language, vision, and protein sequences have inspired the development of large genomic DNA language models (DNALMs). These mod…
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
AkshatKumar Nigam, Robert Pollice, Gary Tom +5
The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the mos…
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…
The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny +17
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines…
A multi-modal neural network for learning cis and trans regulation of stress response in yeast
Boxiang Liu, Nadine Hussami, Avanti Shrikumar +5
Deciphering gene regulatory networks is a central problem in computational biology. Here, we explore the use of multi-modal neural networks to learn predictive models of gene expre…
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Amr Alexandari, Anshul Kundaje, Avanti Shrikumar
Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixe…