Search Results for author: Rick Stevens

Found 16 papers, 7 papers with code

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).

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.

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

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.

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.

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).

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.

Transfer Learning

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

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.

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

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.

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.

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.