S. Joshua Joshua Swamidass

发表

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

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

Learning a Local-Variable Model of Aromatic and Conjugated Systems

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

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

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

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

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

Tumor Suppressor Heterogeneous Biomedical Database Integration Using a Hybrid Strategy

S. Joshua Joshua Swamidass, Dr. Pierre Baldi, 2005 .

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.

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

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

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

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

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

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

Unsupervised detection of cancer driver mutations with parsimony-guided learning

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

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.

Accounting for proximal variants improves neoantigen prediction

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

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.

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.

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

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

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

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

Opportunities and obstacles for deep learning in biology and medicine

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

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

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

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

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

Precision Medicine in Pancreatic Disease—Knowledge Gaps and Research Opportunities

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

COMPUTATIONAL APPROACHES TO DRUG REPURPOSING AND PHARMACOLOG- SESSION INTRODUCTION

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

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

Alessandro, Serge, Tyler B. Hughes, 2016 .

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

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

The Overlooked Science of Genealogical Ancestry

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

Subcellular Localization and Ser-137 Phosphorylation Regulate Tumor-suppressive Activity of Profilin-1*

R. Pappu, S. Joshua Joshua Swamidass, M. Bissell, 2015, The Journal of Biological Chemistry.

Session Introduction

Olivier Bodenreider, Yves A. Lussier, Robert Stevens, 2005, Pacific Symposium on Biocomputing.

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 .

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.

A simple model predicts UGT-mediated metabolism

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

Sharing Chemical Relationships Does Not Reveal Structures

Sanjay Joshua Swamidass, Matthew K. Matlock, S. Joshua Joshua Swamidass, 2014, 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.

Functional census of mutation sequence spaces: the example of p53 cancer rescue mutants

Pierre Baldi, Ray Luo, Sanjay Joshua Swamidass, 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

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

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.

Graph kernels for chemical informatics

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

XenoNet: Inference and Likelihood of Intermediate Metabolite Formation

S Joshua Swamidass, S. Joshua Swamidass, Na Le Dang, 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.

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

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

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

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

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

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

Evolution and Functional Information

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

Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity

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

The Biological Meaning of Race

S. Joshua Joshua Swamidass, 2020 .

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

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

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.

ChemDB update - full-text search and virtual chemical space

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

A survey of current trends in computational drug repositioning

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

Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam.

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

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

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

Standard operating procedure for somatic variant refinement of tumor sequencing data

Malachi Griffith, Obi L. Griffith, Sanjay Joshua Swamidass, 2018, bioRxiv.

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

Sanjay Joshua Swamidass, Charles Bergeron, Patrik Rydberg, 2013, Bioinform..

‘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.

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

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

Mining small-molecule screens to repurpose drugs

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

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..

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

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

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.

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..

Statistical Distribution of Chemical Fingerprints

Pierre Baldi, Sanjay Joshua Swamidass, P. Baldi, 2005, WILF.

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..

Extending P450 site-of-metabolism models with region-resolution data

Sanjay Joshua Swamidass, Tyler B. Hughes, Jed Zaretzki, 2015, Bioinform..

Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints

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

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

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

Accounting for noise when clustering biological data

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

Brief Population Bottlenecks Are Beyond The Genetic Streetlight

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

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.

Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity.

S. Joshua Joshua Swamidass, Ting Wang, A. Kim, 2016, Scientific reports.

Enhancing the rate of scaffold discovery with diversity-oriented prioritization

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

Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity

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

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

Pierre Baldi, Sanjay Joshua Swamidass, Chloé-Agathe Azencott, 2009, J. Chem. Inf. Model..

Managing missing measurements in small-molecule screens

Sanjay Joshua Swamidass, Bradley T. Calhoun, Michael R. Browning, 2013, Journal of Computer-Aided Molecular Design.

Probabilistic Substructure Mining From Small‐Molecule Screens

S Joshua Swamidass, Sayan Ranu, Bradley T Calhoun, 2011, Molecular informatics.

Scaffold network generator: a tool for mining molecular structures

Sanjay Joshua Swamidass, Matthew K. Matlock, Jed Zaretzki, 2013, Bioinform..

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).

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..

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.