S. Joshua Swamidass

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Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples

Obi L. Griffith, Malachi Griffith, Nicholas C. Spies, 2018, Genetics in Medicine.

COMPUTATIONAL APPROACHES TO DRUG REPURPOSING AND PHARMACOLOG- SESSION INTRODUCTION

Atul J. Butte, Pankaj K. Agarwal, S. Joshua Swamidass, 2013 .

Evolution and Functional Information

Matthew Matlock, S. Joshua Swamidass, Matthew K. Matlock, 2017, bioRxiv.

Securely Measuring the Overlap between Private Datasets with Cryptosets

Matthew Matlock, S. Joshua Swamidass, Leon Rozenblit, 2015, PloS one.

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

S. Joshua Swamidass, Matthew K. Matlock, Joseph P. Gaut, 2018, bioRxiv.

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

S. Joshua Swamidass, Tyler B. Hughes, G. Miller, 2015, ACS central science.

One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties.

Pierre Baldi, Liva Ralaivola, S. Joshua Swamidass, 2007 .

Bounds and Algorithms for Fast Exact Searches of Chemical Fingerprints in Linear and Sublinear Time.

Pierre Baldi, S. Joshua Swamidass, P. Baldi, 2007 .

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Matthew Matlock, S. Joshua Swamidass, Joseph P. Gaut, 2018 .

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism.

S. Joshua Swamidass, Tyler B. Hughes, S. Joshua Joshua Swamidass, 2017, Chemical research in toxicology.

Opportunities and obstacles for deep learning in biology and medicine

Anne E Carpenter, Srinivas C. Turaga, Brett K. Beaulieu-Jones, 2017, bioRxiv.

Deep learning long-range information in undirected graphs with wave networks

Sanjay Joshua Swamidass, S. Joshua Swamidass, Kevin Jiang, 2018, 2019 International Joint Conference on Neural Networks (IJCNN).

Graph kernels for chemical informatics

Pierre Baldi, Sanjay Joshua Swamidass, Hiroto Saigo, 2005, Neural Networks.

A survey of current trends in computational drug repositioning

Zhiyong Lu, Bin Chen, Atul J. Butte, 2016, Briefings Bioinform..

Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Pierre Baldi, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2015, Journal of Cheminformatics.

ChemDB: a public database of small molecules and related chemoinformatics resources

Pierre Baldi, Sanjay Joshua Swamidass, Jonathan H. Chen, 2005, Bioinform..

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity

Pierre Baldi, Sanjay Joshua Swamidass, Liva Ralaivola, 2005, ISMB.

Accounting for proximal variants improves neoantigen prediction

S. Joshua Swamidass, E. Mardis, O. Griffith, 2018, Nature Genetics.

XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

Sanjay Joshua Swamidass, S. Joshua Swamidass, Matthew K. Matlock, 2013, J. Chem. Inf. Model..

Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

Manuel Llinás, George Papadatos, Sundari Suresh, 2016, PLoS pathogens.

The Overlooked Science of Genealogical Ancestry

S. Joshua Swamidass, S. Joshua Joshua Swamidass, Joshua Swamidass, 2017 .

A Time-Embedding Network Models the Ontogeny of 23 Hepatic Drug Metabolizing Enzymes.

S. Joshua Swamidass, Matthew K. Matlock, G. Miller, 2019, Chemical research in toxicology.

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data

Benjamin J. Ainscough, Erica K. Barnell, Katie M. Campbell, 2018, Nature Genetics.

Genome analysis Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data

S. Joshua Swamidass, Adam C. Searleman, O. Griffith, 2015 .

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples

Benjamin J. Ainscough, Erica K. Barnell, Katie M. Campbell, 2018, Genetics in Medicine.

‘Black Box’ to ‘Conversational’ Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism

S. Joshua Swamidass, Matthew K. Matlock, N. Dang, 2020, IEEE Journal of Biomedical and Health Informatics.

ChemDB update - full-text search and virtual chemical space

Pierre Baldi, Sanjay Joshua Swamidass, Dennis Wang, 2007, Bioinform..

The end of evolution?

S. Joshua Swamidass, Nathan H. Lents, R. Lenski, 2019, Science.

Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes.

S. Joshua Swamidass, Tyler B. Hughes, N. Dang, 2017, Chemical research in toxicology.

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

S. Joshua Swamidass, Tyler B. Hughes, N. Dang, 2016, ACS central science.

Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity

S. Joshua Swamidass, S. Joshua Joshua Swamidass, Ting Wang, 2016, Scientific Reports.

Combined Analysis of Phenotypic and Target-Based Screening in Assay Networks

Pankaj Agarwal, S Joshua Swamidass, Matthew Matlock, 2014, Journal of biomolecular screening.

Unsupervised detection of cancer driver mutations with parsimony-guided learning

S Joshua Swamidass, Ron Bose, S. Joshua Swamidass, 2016, Nature Genetics.

Bigger data, collaborative tools and the future of predictive drug discovery

Antony J. Williams, Alex M. Clark, Sanjay Joshua Swamidass, 2014, Journal of Computer-Aided Molecular Design.

Accounting for noise when clustering biological data

Sanjay Joshua Swamidass, Roman Sloutsky, Kristen M. Naegle, 2012, Briefings Bioinform..

Precision Medicine in Pancreatic Disease—Knowledge Gaps and Research Opportunities

S. Joshua Swamidass, J. Gray, S. Plevritis, 2019, Pancreas.

Mining small-molecule screens to repurpose drugs

Sanjay Joshua Swamidass, S. Joshua Swamidass, S. Joshua Joshua Swamidass, 2011, Briefings Bioinform..

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

S Joshua Swamidass, Jayme L. Dahlin, S. Joshua Swamidass, 2018, J. Chem. Inf. Model..

Bounds and Algorithms for Fast Exact Searches of Chemical Fingerprints in Linear and Sublinear Time

Pierre Baldi, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2007, J. Chem. Inf. Model..

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

S Joshua Swamidass, S. Joshua Swamidass, Ayush Kumar, 2020, J. Chem. Inf. Model..

Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches

S. Joshua Swamidass, Tyler B. Hughes, N. Dang, 2018, Biochemical pharmacology.

XenoSite server: a web-available site of metabolism prediction tool

Sanjay Joshua Swamidass, S. Joshua Swamidass, Matthew K. Matlock, 2015, Bioinform..

Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione.

S. Joshua Swamidass, Tyler B. Hughes, G. Miller, 2015, Chemical research in toxicology.

BEESEM: estimation of binding energy models using HT‐SELEX data

Shuxiang Ruan, Sanjay Joshua Swamidass, Gary D. Stormo, 2017, Bioinform..

Structure-based inhibitor design of AccD5, an essential acyl-CoA carboxylase carboxyltransferase domain of Mycobacterium tuberculosis.

Pierre Baldi, S Joshua Swamidass, S. Joshua Swamidass, 2006, Proceedings of the National Academy of Sciences of the United States of America.

Enhancing the rate of scaffold discovery with diversity-oriented prioritization

Sanjay Joshua Swamidass, S. Joshua Swamidass, Paul A. Clemons, 2011, Bioinform..

Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam.

S. Joshua Swamidass, Matthew K. Matlock, Noah R. Flynn, 2020, Toxicology.

Learning a Local-Variable Model of Aromatic and Conjugated Systems

S. Joshua Swamidass, Matthew K. Matlock, N. Dang, 2018, ACS central science.

A simple model predicts UGT-mediated metabolism

Sanjay Joshua Swamidass, S. Joshua Swamidass, Varun Krishnamurthy, 2016, Bioinform..

Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints

Sanjay Joshua Swamidass, S. Joshua Swamidass, Jed Zaretzki, 2015, J. Chem. Inf. Model..

XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

S. Joshua Swamidass, Matthew K. Matlock, Jed Zaretzki, 2013, J. Chem. Inf. Model..

Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method

S. Joshua Swamidass, P. Baldi, Chloé-Agathe Azencott, 2009, J. Chem. Inf. Model..

Bridging the Gap Between Neural Network and Kernel Methods: Applications to Drug Discovery

Pierre Baldi, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2011, WIRN.

A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval

Pierre Baldi, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2010, Bioinform..

One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties

Pierre Baldi, Sanjay Joshua Swamidass, Liva Ralaivola, 2007, J. Chem. Inf. Model..

The Influence Relevance Voter : An Accurate And Interpretable Virtual High Throughput Screening Method

S. Joshua Swamidass, P. Baldi, S. Joshua Joshua Swamidass, 2009 .

Opportunities and obstacles for deep learning in biology and medicine [update in progress]

Anne E Carpenter, Srinivas C. Turaga, Brett K. Beaulieu-Jones, 2020 .

Opportunities and obstacles for deep learning in biology and medicine: 2019 update

Anne E Carpenter, Srinivas C. Turaga, Brett K. Beaulieu-Jones, 2019 .

Brief Population Bottlenecks Are Beyond The Genetic Streetlight

S. Joshua Swamidass, S. Joshua Joshua Swamidass, Jack Elliott-Higgins, 2021 .

The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors

S Joshua Swamidass, S. Joshua Swamidass, Matthew K. Matlock, 2020, J. Chem. Inf. Model..

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

S Joshua Swamidass, S. Joshua Swamidass, Tyler B. Hughes, 2018, Chemical research in toxicology.

Disentangling Socioeconomic Status and Race in Infant Outcomes: A Neural Network Analysis

S. Joshua Swamidass, D. Barch, J. Miller, 2021, medRxiv.

Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data

Obi L. Griffith, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2015, Bioinform..

Enhancing the rate of scaffold discovery with diversity-oriented prioritization

S. Joshua Swamidass, Joshua A. Bittker, Bradley T. Calhoun, 2011, Bioinform..

An Economic Framework to Prioritize Confirmatory Tests after a High-Throughput Screen

S Joshua Swamidass, Joshua A Bittker, Nicole E Bodycombe, 2010, Journal of biomolecular screening.

Lossless Compression of Chemical Fingerprints Using Integer Entropy Codes Improves Storage and Retrieval

Pierre Baldi, Sanjay Joshua Swamidass, Daniel S. Hirschberg, 2007, J. Chem. Inf. Model..

Discovery of Power-Laws in Chemical Space

Pierre Baldi, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2008, J. Chem. Inf. Model..

Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval

Pierre Baldi, Sanjay Joshua Swamidass, S. Joshua Swamidass, 2007, J. Chem. Inf. Model..

Fold recognition by combining profile-profile alignment and support vector machine

Chan-seok Jeong, Dongsup Kim, Sangjo Han, 2005, Bioinform..

XenoNet: Inference and Likelihood of Intermediate Metabolite Formation

S Joshua Swamidass, S. Joshua Swamidass, Na Le Dang, 2020, J. Chem. Inf. Model..

RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules

S. Joshua Swamidass, C. Breneman, Charles Bergeron, 2013, Bioinform..

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data

S Joshua Swamidass, Malachi Griffith, Benjamin J. Ainscough, 2018, Nature Genetics.

CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches.

S. Joshua Swamidass, Noah R. Flynn, G. Miller, 2019, Chemical research in toxicology.

Using economic optimization to design high-throughput screens.

S Joshua Swamidass, S. Joshua Swamidass, 2013, Future medicinal chemistry.

GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis

S. Joshua Swamidass, O. Griffith, M. Inkman, 2024, Patterns.

Becker Digital Commons@Becker

S. Joshua Swamidass, X. Xing, S. Pondugula, 2022 .