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.
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.
1 code implementation • 13 Jun 2021 • Austin Clyde, Thomas Brettin, Alexander Partin, Hyunseung Yoo, Yadu Babuji, Ben Blaiszik, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens
Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).
2 code implementations • 28 May 2021 • Zhengchun Liu, Ahsan Ali, Peter Kenesei, Antonino Miceli, Hemant Sharma, Nicholas Schwarz, Dennis Trujillo, Hyunseung Yoo, Ryan Coffee, Naoufal Layad, Jana Thayer, Ryan Herbst, ChunHong Yoon, Ian Foster
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes.
no code implementations • 18 Apr 2021 • Fangfang Xia, Jonathan Allen, Prasanna Balaprakash, Thomas Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith Cohn, James Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric Stahlberg, Justin M. Wozniak, Hyunseung Yoo, George Zaki, Yitan Zhu, Rick Stevens
To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI.
1 code implementation • 25 Nov 2020 • Alexander Partin, Thomas Brettin, Yvonne A. Evrard, Yitan Zhu, Hyunseung Yoo, Fangfang Xia, Songhao Jiang, Austin Clyde, Maulik Shukla, Michael Fonstein, James H. Doroshow, Rick Stevens
In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes.
no code implementations • 13 May 2020 • Yitan Zhu, Thomas Brettin, Yvonne A. Evrard, Alexander Partin, Fangfang Xia, Maulik Shukla, Hyunseung Yoo, James H. Doroshow, Rick Stevens
Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment.
1 code implementation • 30 Apr 2020 • Austin Clyde, Tom Brettin, Alexander Partin, Maulik Shaulik, Hyunseung Yoo, Yvonne Evrard, Yitan Zhu, Fangfang Xia, Rick Stevens
By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions.