2.3k citations · 2.5k across the 8 of their papers we have counts for
9 papers
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Charlotte Bunne, Yusuf Roohani, Yanay Rosen +39
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determ…
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Yusuf Roohani, Andrew Lee, Qian Huang +6
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In t…
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…
Zero-shot causal learning
Hamed Nilforoshan, Michael Moor, Yusuf Roohani +5
Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online mark…
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou +2
Causal inference is a vital aspect of multiple scientific disciplines and is routinely applied to high-impact applications such as medicine. However, evaluating the performance of…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Kexin Huang, Tianfan Fu, Wenhao Gao +7
Therapeutics machine learning is an emerging field with incredible opportunities for innovatiaon and impact. However, advancement in this field requires formulation of meaningful l…
Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI
Yusuf H. Roohani, Noor Sajid, Pranava Madhyastha +2
One third of stroke survivors have language difficulties. Emerging evidence suggests that their likelihood of recovery depends mainly on the damage to language centers. Thus previo…