no code implementations • 17 Oct 2023 • Priyanka Vasanthakumari, Thomas Brettin, Yitan Zhu, Hyunseung Yoo, Maulik Shukla, Alexander Partin, Fangfang Xia, Oleksandr Narykov, Rick L. Stevens
Several error analysis metrics such as the false positive rate (FPR), and the prediction uncertainty are evaluated, and the results are summarized by cancer type and drug mechanism of action (MoA) category.
no code implementations • 3 Oct 2023 • Xuefeng Liu, Takuma Yoneda, Rick L. Stevens, Matthew R. Walter, Yuxin Chen
Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner's performance surpasses that of the oracles in a specific state.
1 code implementation • 17 Jul 2023 • Wei Chen, Yihui Ren, Ai Kagawa, Matthew R. Carbone, Samuel Yen-Chi Chen, Xiaohui Qu, Shinjae Yoo, Austin Clyde, Arvind Ramanathan, Rick L. Stevens, Hubertus J. J. van Dam, Deyu Lu
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
no code implementations • 18 Nov 2022 • Alexander Partin, Thomas S. Brettin, Yitan Zhu, Oleksandr Narykov, Austin Clyde, Jamie Overbeek, Rick L. Stevens
A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods.
no code implementations • 13 Jul 2022 • Xuefeng Liu, Fangfang Xia, Rick L. Stevens, Yuxin Chen
In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection.
1 code implementation • 25 Apr 2022 • Alexander Partin, Thomas Brettin, Yitan Zhu, James M. Dolezal, Sara Kochanny, Alexander T. Pearson, Maulik Shukla, Yvonne A. Evrard, James H. Doroshow, Rick L. Stevens
Prediction performance of three unimodal NNs which use GE are compared to assess the contribution of data augmentation methods.
1 code implementation • Scientific Reports 2021 • Yitan Zhu, Thomas Brettin, Fangfang Xia, Alexander Partin, Maulik Shukla, Hyunseung Yoo, Yvonne A. Evrard, James H. Doroshow, Rick L. Stevens
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging.
4 code implementations • ICLR 2022 • Yulun Wu, Mikaela Cashman, Nicholas Choma, Érica T. Prates, Verónica G. Melesse Vergara, Manesh Shah, Andrew Chen, Austin Clyde, Thomas S. Brettin, Wibe A. de Jong, Neeraj Kumar, Martha S. Head, Rick L. Stevens, Peter Nugent, Daniel A. Jacobson, James B. Brown
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.