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Sanjay Joshua Swamidass
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- affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
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2020 – today
- 2024
- [j39]Michael R. Waters, Matthew Inkman
, Kay Jayachandran, Roman M. Kowalchuk, Clifford Robinson, Julie K. Schwarz, S. Joshua Swamidass, Obi L. Griffith, Jeffrey J. Szymanski, Jin Zhang
GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis. Patterns 5(2): 100910 (2024) - 2023
- [j38]Noah R. Flynn
Message Passing Neural Networks Improve Prediction of Metabolite Authenticity. J. Chem. Inf. Model. 63(6): 1675-1694 (2023) - 2022
- [c8]Arghya Datta, S. Joshua Swamidass:
Fair-Net: A Network Architecture for Reducing Performance Disparity between Identifiable Sub-populations. ICAART (3) 2022: 645-654 - 2021
- [j37]Arghya Datta
Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort. PLoS Comput. Biol. 17(7) (2021) - [j36]Arghya Datta
'Black Box' to 'Conversational' Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism. IEEE J. Biomed. Health Informatics 25(6): 2204-2214 (2021) - [c7]Arghya Datta
Cal-Net: Jointly Learning Classification and Calibration On Imbalanced Binary Classification Tasks. IJCNN 2021: 1-8 - [i2]Arghya Datta
Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populations. CoRR abs/2106.00720 (2021) - 2020
- [j35]Na Le Dang
The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors. J. Chem. Inf. Model. 60(3): 1146-1164 (2020) - [j34]Noah R. Flynn
XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J. Chem. Inf. Model. 60(7): 3431-3449 (2020) - [j33]Tyler B. Hughes
Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J. Chem. Inf. Model. 60(10): 4702-4716 (2020)
2010 – 2019
- 2019
- [c6]Matthew K. Matlock, Arghya Datta, Na Le Dang, Kevin Jiang, S. Joshua Swamidass
Deep learning long-range information in undirected graphs with wave networks. IJCNN 2019: 1-8 - 2018
- [j32]Matthew K. Matlock
Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds. J. Chem. Inf. Model. 58(8): 1483-1500 (2018) - [j31]Jon N. Marsh
Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections. IEEE Trans. Medical Imaging 37(12): 2718-2728 (2018) - [c5]William M. Southerland, S. Joshua Swamidass, Philip R. O. Payne, Laura K. Wiley, ClarLynda R. Williams-DeVane:
The diversity and disparity in biomedical informatics (DDBI) workshop. PSB 2018: 614-617 - [i1]Matthew K. Matlock, Arghya Datta, Na Le Dang, Kevin Jiang, S. Joshua Swamidass:
Deep learning long-range information in undirected graphs with wave networks. CoRR abs/1810.12153 (2018) - 2017
- [j30]Shuxiang Ruan
BEESEM: estimation of binding energy models using HT-SELEX data. Bioinform. 33(15): 2288-2295 (2017) - 2016
- [j29]Jiao Li
A survey of current trends in computational drug repositioning. Briefings Bioinform. 17(1): 2-12 (2016) - [j28]Na Le Dang
A simple model predicts UGT-mediated metabolism. Bioinform. 32(20): 3183-3189 (2016) - 2015
- [j27]Matthew K. Matlock
XenoSite server: a web-available site of metabolism prediction tool. Bioinform. 31(7): 1136-1137 (2015) - [j26]Jed M. Zaretzki, Michael R. Browning, Tyler B. Hughes, S. Joshua Swamidass
Extending P450 site-of-metabolism models with region-resolution data. Bioinform. 31(12): 1966-1973 (2015) - [j25]Runjun D. Kumar, Adam C. Searleman
Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data. Bioinform. 31(22): 3561-3568 (2015) - [j24]Alessandro Lusci, Michael R. Browning, David Fooshee, S. Joshua Swamidass
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. J. Cheminformatics 7: 63:1-63:13 (2015) - [j23]Jed Zaretzki, Kevin Michael Boehm, Sanjay Joshua Swamidass
Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints. J. Chem. Inf. Model. 55(5): 972-982 (2015) - 2014
- [j22]Sean Ekins, Alex M. Clark, S. Joshua Swamidass
Bigger data, collaborative tools and the future of predictive drug discovery. J. Comput. Aided Mol. Des. 28(10): 997-1008 (2014) - [j21]Matthew K. Matlock
Sharing Chemical Relationships Does Not Reveal Structures. J. Chem. Inf. Model. 54(1): 37-48 (2014) - [c4]S. Joshua Swamidass, Zhiyong Lu, Pankaj Agarwal, Atul J. Butte:
Session Introduction. Pacific Symposium on Biocomputing 2014: 110-113 - 2013
- [j20]Roman Sloutsky
Accounting for noise when clustering biological data. Briefings Bioinform. 14(4): 423-436 (2013) - [j19]Jed Zaretzki, Charles Bergeron, Tao-wei Huang, Patrik Rydberg, S. Joshua Swamidass
RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules. Bioinform. 29(4): 497-498 (2013) - [j18]Matthew K. Matlock
Scaffold network generator: a tool for mining molecular structures. Bioinform. 29(20): 2655-2656 (2013) - [j17]Michael R. Browning, Bradley T. Calhoun, Sanjay Joshua Swamidass
Managing missing measurements in small-molecule screens. J. Comput. Aided Mol. Des. 27(5): 469-478 (2013) - [j16]Jed Zaretzki, Matthew K. Matlock
XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks. J. Chem. Inf. Model. 53(12): 3373-3383 (2013) - 2012
- [j15]S. Joshua Swamidass
Utility-Aware Screening with Clique-Oriented Prioritization. J. Chem. Inf. Model. 52(1): 29-37 (2012) - 2011
- [j14]Sanjay Joshua Swamidass
Mining small-molecule screens to repurpose drugs. Briefings Bioinform. 12(4): 327-335 (2011) - [j13]Sanjay Joshua Swamidass
Enhancing the rate of scaffold discovery with diversity-oriented prioritization. Bioinform. 27(16): 2271-2278 (2011) - 2010
- [j12]Sanjay Joshua Swamidass
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval. Bioinform. 26(10): 1348-1356 (2010) - [c3]Pierre Baldi, Chloé-Agathe Azencott, Sanjay Joshua Swamidass
Bridging the Gap Between Neural Network and Kernel Methods: Applications to Drug Discovery. WIRN 2010: 3-13
2000 – 2009
- 2009
- [j11]Ramzi Nasr, Sanjay Joshua Swamidass
Large scale study of multiple-molecule queries. J. Cheminformatics 1: 7 (2009) - [j10]S. Joshua Swamidass
Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method. J. Chem. Inf. Model. 49(4): 756-766 (2009) - 2008
- [j9]Ryan W. Benz
Discovery of Power-Laws in Chemical Space. J. Chem. Inf. Model. 48(6): 1138-1151 (2008) - 2007
- [j8]Jonathan H. Chen
ChemDB update - full-text search and virtual chemical space. Bioinform. 23(17): 2348-2351 (2007) - [j7]S. Joshua Swamidass
Bounds and Algorithms for Fast Exact Searches of Chemical Fingerprints in Linear and Sublinear Time. J. Chem. Inf. Model. 47(2): 302-317 (2007) - [j6]S. Joshua Swamidass
Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval. J. Chem. Inf. Model. 47(3): 952-964 (2007) - [j5]Chloé-Agathe Azencott
One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. J. Chem. Inf. Model. 47(3): 965-974 (2007) - [j4]Pierre Baldi, Ryan W. Benz
Lossless Compression of Chemical Fingerprints Using Integer Entropy Codes Improves Storage and Retrieval. J. Chem. Inf. Model. 47(6): 2098-2109 (2007) - 2006
- [j3]Samuel A. Danziger, Sanjay Joshua Swamidass
Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants. IEEE ACM Trans. Comput. Biol. Bioinform. 3(2): 114-125 (2006) - 2005
- [j2]Jonathan H. Chen
ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinform. 21(22): 4133-4139 (2005) - [j1]Liva Ralaivola, Sanjay Joshua Swamidass
Graph kernels for chemical informatics. Neural Networks 18(8): 1093-1110 (2005) - [c2]Sanjay Joshua Swamidass
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. ISMB (Supplement of Bioinformatics) 2005: 359-368 - [c1]Sanjay Joshua Swamidass, Pierre Baldi:
Statistical Distribution of Chemical Fingerprints. WILF 2005: 11-18
Coauthor Index
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