Search Results for author: Rick Stevens

Found 20 papers, 9 papers with code

Machine Learning for Antimicrobial Resistance

no code implementations5 Jul 2016 John W. Santerre, James J. Davis, Fangfang Xia, Rick Stevens

Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community.

BIG-bench Machine Learning General Classification

Precision Medicine as an Accelerator for Next Generation Cognitive Supercomputing

no code implementations29 Apr 2018 Edmon Begoli, Jim Brase, Bambi DeLaRosa, Penelope Jones, Dimitri Kusnezov, Jason Paragas, Rick Stevens, Fred Streitz, Georgia Tourassi

In the past several years, we have taken advantage of a number of opportunities to advance the intersection of next generation high-performance computing AI and big data technologies through partnerships in precision medicine.

Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research

no code implementations1 Sep 2019 Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens

Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power.

Neural Architecture Search reinforcement-learning +1

A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning

1 code implementation30 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.

Data Integration

Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing

no code implementations11 May 2020 Neil Getty, Thomas Brettin, Dong Jin, Rick Stevens, Fangfang Xia

We explore three representative lines of research and demonstrate the utility of our methods on a classification benchmark of brain cancer MRI data.

Domain Adaptation Representation Learning

Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response

no code implementations13 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.

Drug Response Prediction Transfer Learning

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

1 code implementation28 May 2020 Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

BIG-bench Machine Learning

Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models

1 code implementation1 Jun 2020 Austin Clyde, Xiaotian Duan, Rick Stevens

We present a new method for understanding the performance of a model in virtual drug screening tasks.

regression

Learning Curves for Drug Response Prediction in Cancer Cell Lines

1 code implementation25 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.

Drug Response Prediction

Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds

no code implementations11 Mar 2021 Austin Clyde, Arvind Ramanathan, Rick Stevens

Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data.

Drug Discovery Representation Learning

Neko: a Library for Exploring Neuromorphic Learning Rules

1 code implementation1 May 2021 Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia

To address this gap, we present Neko, a modular, extensible library with a focus on aiding the design of new learning algorithms.

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening

1 code implementation13 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).

Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery

1 code implementation6 Apr 2023 Ashka Shah, Arvind Ramanathan, Valerie Hayot-Sasson, Rick Stevens

Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e. g. differences in cell function, disease, drug resistance and others.

Causal Discovery Experimental Design

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies

no code implementations6 Oct 2023 Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi Hanson, Thomas E Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin Aji, Angela Dalton, Michael Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.

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