8 citations · 13 across the 7 of their papers we have counts for
7 papers
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
Jamie C. Overbeek, Alexander Partin, Thomas S. Brettin +21
Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL)…
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment
Priyanka Vasanthakumari, Thomas Brettin, Yitan Zhu +6
Informed selection of drug candidates for laboratory experimentation provides an efficient means of identifying suitable anti-cancer treatments. The advancement of artificial intel…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
Wei Chen, Yihui Ren, Ai Kagawa +9
Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for develo…
Towards a Modular Architecture for Science Factories
Rafael Vescovi, Tobias Ginsburg, Kyle Hippe +14
Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories: large, general-purpose computation…