SVDMAN-singular value decomposition analysis of microarray data. - Michael E. Wall; Patricia A. Dyck; Thomas S. Brettin (Bioinform., 2001)

BioFilter: An Architecture for Parallel Deployment and Dynamic Chaining of Standalone Bioinformatics Tools. - Thomas S. Brettin; Avinash Kewalramani (IPDPS, 2005)

A novel approach to high throughput microbial genome finishing: Incorporation of 454 sequence data for gap closure in low quality Sanger data. - Avinash Kewalramani; Riley Arnaudville; Cliff Han; Olga Chertkov; Thomas S. Brettin (BIOCOMP, 2007)

Streptococcus in Toto: A Species-specific Comparative Analysis Website Prototype. - Roxanne Tapia; Gary Xie; Ravi D. Barabote; Gerry Myers; Thomas S. Brettin (BIOCOMP, 2008)

Next generation models for storage and representation of microbial biological annotation. - Daniel Quest; Miriam L. Land; Thomas S. Brettin; Robert W. Cottingham (BMC Bioinform., 2010)

Poster: a novel architecture for biothreat situation awareness. - Thomas S. Brettin; Laura Pullum; Daniel Quest; Xiaohui Cui; Songhua Xu; Richard Stouder (SC Companion, 2011)

Scenario driven data modelling: a method for integrating diverse sources of data and data streams. - Shelton D. Griffith; Daniel Quest; Thomas S. Brettin; Robert W. Cottingham (BMC Bioinform., 2011)

RegPrecise web services interface: programmatic access to the transcriptional regulatory interactions in bacteria reconstructed by comparative genomics. - Pavel S. Novichkov; Thomas S. Brettin; Elena S. Novichkova; Paramvir S. Dehal; Adam P. Arkin; Inna Dubchak; Dmitry A. Rodionov (Nucleic Acids Res., 2012)

A RESTful API for Accessing Microbial Community Data for MG-RAST. - Andreas Wilke; Jared Bischof; Travis Harrison; Tom Brettin; Mark D'Souza; Wolfgang Gerlach; Hunter Matthews; Tobias Paczian; Jared Wilkening; Elizabeth M. Glass; Narayan Desai; Folker Meyer (PLoS Comput. Biol., 2015)

Leveraging Large-Scale Computing for Population Information Integration, Analysis, and Modeling. - Jessica A. Boten; Donna R. Rivera; Madhumita Myneni; Georgia D. Tourassi; Tanmoy Bhattacharya 001; Ana Paula de Oliveira Sales; Thomas S. Brettin; Paul A. Fearn; Lynne Penberthy (AMIA, 2017)

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. - Alice R. Wattam; James J. Davis 002; Rida Assaf; Sébastien Boisvert; Thomas S. Brettin; Christopher Bun; Neal Conrad; Emily M. Dietrich; Terry Disz; Joseph L. Gabbard; Svetlana Gerdes; Christopher S. Henry; et al. (Nucleic Acids Res., 2017)

Portable and Reusable Deep Learning Infrastructure with Containers to Accelerate Cancer Studies. - George F. Zaki; Justin M. Wozniak; Jonathan Ozik; Nicholson T. Collier; Thomas S. Brettin; Rick Stevens (ESPM2@SC, 2018)

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research. - Justin M. Wozniak; Rajeev Jain; Prasanna Balaprakash; Jonathan Ozik; Nicholson T. Collier; John Bauer; Fangfang Xia; Thomas S. Brettin; Rick Stevens; Jamaludin Mohd-Yusof; Cristina Garcia-Cardona; Brian Van Essen; et al. (BMC Bioinform., 2018)

Predicting tumor cell line response to drug pairs with deep learning. - Fangfang Xia; Maulik Shukla; Thomas S. Brettin; Cristina Garcia-Cardona; Judith D. Cohn; Jonathan E. Allen; Sergei Maslov; Susan L. Holbeck; James H. Doroshow; Yvonne A. Evrard; Eric A. Stahlberg; Rick L. Stevens (BMC Bioinform., 2018)

Performance, Energy, and Scalability Analysis and Improvement of Parallel Cancer Deep Learning CANDLE Benchmarks. - Xingfu Wu; Valerie E. Taylor; Justin M. Wozniak; Rick Stevens; Thomas S. Brettin; Fangfang Xia (ICPP, 2019)

Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. - Prasanna Balaprakash; Romain Egele; Misha Salim; Stefan M. Wild; Venkatram Vishwanath; Fangfang Xia; Tom Brettin; Rick Stevens (SC, 2019)

PATRIC as a unique resource for studying antimicrobial resistance. - Dionysios A. Antonopoulos; Rida Assaf; Ramy Karam Aziz; Thomas S. Brettin; Christopher Bun; Neal Conrad; James J. Davis 002; Emily M. Dietrich; Terry Disz; Svetlana Gerdes; Ronald W. Kenyon; Dustin Machi; et al. (Briefings Bioinform., 2019)

High-bypass Learning: Automated Detection of Tumor Cells That Significantly Impact Drug Response. - Justin M. Wozniak; Hyun Seung Yoo; Jamaludin Mohd-Yusof; Bogdan Nicolae; Nicholson T. Collier; Jonathan Ozik; Thomas S. Brettin; Rick Stevens (MLHPC/AI4S@SC, 2020)

A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning. - Austin Clyde; Tom Brettin; Alexander Partin; Maulik Shaulik; Hyun Seung Yoo; Yvonne A. Evrard; Yitan Zhu; Fangfang Xia; Rick Stevens (CoRR, 2020)

Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing. - Neil Getty; Thomas S. Brettin; Dong Jin; Rick Stevens; Fangfang Xia (CoRR, 2020)

Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response. - Yitan Zhu; Thomas S. Brettin; Yvonne A. Evrard; Alexander Partin; Fangfang Xia; Maulik Shukla; Hyun Seung Yoo; James H. Doroshow; Rick Stevens (CoRR, 2020)

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release. - Yadu N. Babuji; Ben Blaiszik; Tom Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Ian T. Foster; Zhi Hong; Shantenu Jha; Zhuozhao Li; Xuefeng Liu; Arvind Ramanathan; et al. (CoRR, 2020)

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. - James J. Davis 002; Alice R. Wattam; Ramy K. Aziz; Thomas S. Brettin; Ralph Butler; Rory Butler; Philippe Chlenski; Neal Conrad; Allan Dickerman; Emily M. Dietrich; Joseph L. Gabbard; Svetlana Gerdes; et al. (Nucleic Acids Res., 2020)

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. - Aymen Al Saadi; Dario Alfè; Yadu N. Babuji; Agastya Bhati; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Peter V. Coveney; Ian T. Foster; et al. (ICPP, 2021)

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes. - Margo VanOeffelen; Marcus Nguyen; Derya Aytan-Aktug; Thomas S. Brettin; Emily M. Dietrich; Ronald W. Kenyon; Dustin Machi; Chunhong Mao; Robert Olson; Gordon D. Pusch; Maulik Shukla; Rick Stevens; et al. (Briefings Bioinform., 2021)

Learning curves for drug response prediction in cancer cell lines. - Alexander Partin; Thomas S. Brettin; Yvonne A. Evrard; Yitan Zhu; Hyun Seung Yoo; Fangfang Xia; Songhao Jiang; Austin Clyde; Maulik Shukla; Michael Fonstein; James H. Doroshow; Rick L. Stevens (BMC Bioinform., 2021)

Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers. - Agastya P. Bhati; Shunzhou Wan; Dario Alfè; Austin R. Clyde; Mathis Bode; Li Tan; Mikhail Titov; André Merzky; Matteo Turilli; Shantenu Jha; Roger R. Highfield; Walter Rocchia; et al. (CoRR, 2021)

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening. - Austin Clyde; Thomas S. Brettin; Alexander Partin; Hyun Seung Yoo; Yadu N. Babuji; Ben Blaiszik; André Merzky; Matteo Turilli; Shantenu Jha; Arvind Ramanathan; Rick Stevens (CoRR, 2021)

CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models. - Max Zvyagin; Thomas S. Brettin; Arvind Ramanathan; Sumit Kumar Jha 001 (CoRR, 2021)

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery. - Yulun Wu; Nicholas Choma; Andrew Deru Chen; Mikaela Cashman; Érica Teixeira Prates; Verónica G. Melesse Vergara; Manesh Shah; Austin Clyde; Thomas S. Brettin; Wibe Albert de Jong; Neeraj Kumar; Martha S. Head; et al. (ICLR, 2022)

A cross-study analysis of drug response prediction in cancer cell lines. - Fangfang Xia; Jonathan E. Allen; Prasanna Balaprakash; Thomas S. Brettin; Cristina Garcia-Cardona; Austin Clyde; Judith D. Cohn; James H. Doroshow; Xiaotian Duan; Veronika Dubinkina; Yvonne A. Evrard; Ya Ju Fan; et al. (Briefings Bioinform., 2022)

Deep learning methods for drug response prediction in cancer: predominant and emerging trends. - Alexander Partin; Thomas S. Brettin; Yitan Zhu; Oleksandr Narykov; Austin Clyde; Jamie C. Overbeek; Rick L. Stevens (CoRR, 2022)

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor. - Austin Clyde; Stephanie Galanie; Daniel W. Kneller; Heng Ma; Yadu N. Babuji; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Leighton Coates; Ian T. Foster; et al. (J. Chem. Inf. Model., 2022)

An Automation Framework for Comparison of Cancer Response Models Across Configurations. - Justin M. Wozniak; Rajeev Jain; Andreas Wilke; Rylie Weaver; Alexander Partin; Thomas S. Brettin; Rick Stevens (e-Science, 2023)

Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction. - Oleksandr Narykov; Yitan Zhu; Thomas S. Brettin; Yvonne A. Evrard; Alexander Partin; Maulik Shukla; Priyanka Vasanthakumari; James H. Doroshow; Rick Stevens (SC Workshops, 2023)

Scalable Lead Prediction with Transformers using HPC resources. - Archit Vasan; Thomas S. Brettin; Rick Stevens; Arvind Ramanathan; Venkatram Vishwanath (SC Workshops, 2023)

Towards a Modular Architecture for Science Factories. - Rafael Vescovi; Tobias Ginsburg; Kyle Hippe; Doga Ozgulbas; Casey Stone; Abraham Stroka; Rory Butler; Ben Blaiszik; Tom Brettin; Kyle Chard; Mark Hereld; Arvind Ramanathan; et al. (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR. - Robert D. Olson; Rida Assaf; Thomas S. Brettin; Neal Conrad; Clark Cucinell; James J. Davis 002; Donald M. Dempsey; Allan Dickerman; Emily M. Dietrich; Ronald W. Kenyon; Mehmet Kuscuoglu; Elliot J. Lefkowitz; et al. (Nucleic Acids Res., 2023)

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. - Maxim Zvyagin; Alexander Brace; Kyle Hippe; Yuntian Deng; Bin Zhang; Cindy Orozco Bohorquez; Austin Clyde; Bharat Kale; Danilo Perez-Rivera; Heng Ma; Carla M. Mann; Michael W. Irvin; et al. (Int. J. High Perform. Comput. Appl., 2023)

High Performance Binding Affinity Prediction with a Transformer-Based Surrogate Model. - Archit Vasan; Ozan Gökdemir; Alexander Brace; Arvind Ramanathan; Thomas S. Brettin; Rick Stevens; Venkatram Vishwanath (IPDPS (Workshops), 2024)

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data. - Jamie C. Overbeek; Alexander Partin; Thomas S. Brettin; Nicholas Chia; Oleksandr Narykov; Priyanka Vasanthakumari; Andreas Wilke; Yitan Zhu; Austin Clyde; Sara Jones; Rohan Gnanaolivu; Yuanhang Liu; et al. (CoRR, 2024)

Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches. - Xuefeng Liu; Songhao Jiang; Xiaotian Duan; Archit Vasan; Chong Liu 007; Chih-chan Tien; Heng Ma; Thomas S. Brettin; Fangfang Xia; Ian T. Foster; Rick L. Stevens (CoRR, 2024)

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights. - Ozan Gökdemir; Carlo Siebenschuh; Alexander Brace; Azton Wells; Brian Hsu; Kyle Hippe; Priyanka Setty; Aswathy Ajith; J. Gregory Pauloski; Varuni Sastry; Sam Foreman; Huihuo Zheng; et al. (PASC, 2025)

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting. - Oleksandr Narykov; Yitan Zhu; Thomas S. Brettin; Yvonne A. Evrard; Alexander Partin; Fangfang Xia; Maulik Shukla; Priyanka Vasanthakumari; James H. Doroshow; Rick L. Stevens (Briefings Bioinform., 2025)

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis. - Alexander Partin; Priyanka Vasanthakumari; Oleksandr Narykov; Andreas Wilke; Natasha Koussa; Sara E. Jones; Yitan Zhu; Jamie C. Overbeek; Rajeev Jain; Gayara Demini Fernando; Cesar Sanchez-Villalobos; Cristina Garcia-Cardona; et al. (CoRR, 2025)

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights. - Ozan Gökdemir; Carlo Siebenschuh; Alexander Brace; Azton Wells; Brian Hsu; Kyle Hippe; Priyanka Vasanthakumari; Aswathy Ajith; J. Gregory Pauloski; Varuni Katti Sastry; Sam Foreman; Huihuo Zheng; et al. (CoRR, 2025)