Michael J. Keiser

发表

Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias pdf

Maria L. Wei, Jacob Pfau, Albert T. Young, 2019, ArXiv.

Quantifying the Relationships among Drug Classes

Tudor I. Oprea, Jérôme Hert, Brian K. Shoichet, 2008, J. Chem. Inf. Model..

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Michael J. Keiser, Brittany N. Dugger, Ziqi Tang, 2018, Nature Communications.

Attention-Based Learning on Molecular Ensembles pdf

Michael J. Keiser, Kangway V. Chuang, 2020, ArXiv.

836 Calibration performance of deep neural networks for image classification declines on real-world, versus curated, test sets

Maria L. Wei, Jacob Pfau, Albert T. Young, 2020 .

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Charles DeCarli, Michael J. Keiser, Brittany N. Dugger, 2018 .

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Deok-Sun Lee, Patricia C. Babbitt, Michael J. Keiser, 2009, PLoS Comput. Biol..

Adding stochastic negative examples into machine learning improves molecular bioactivity prediction

Michael J. Keiser, Elena L. Cáceres, Nicholas C. Mew, 2020, bioRxiv.

Predicted Biological Activity of Purchasable Chemical Space

Michael J. Keiser, Teague Sterling, Michael M. Mysinger, 2017, J. Chem. Inf. Model..

902 Assessing performance of deep neural networks used for image classification by stress testing

Maria L. Wei, Jacob Pfau, Albert T. Young, 2020 .

Deep learning from multiple experts improves identification of amyloid neuropathologies

Michael J. Keiser, Daniel Wong, Daniel R. Wong, 2021 .

Trans-Channel Fluorescence Learning Improves High-Content Screening for Alzheimer's Disease Therapeutics

Atul J. Butte, Michael J. Keiser, Stanley B. Prusiner, 2021 .

Robust Semantic Interpretability: Revisiting Concept Activation Vectors pdf

Maria L. Wei, Jacob Pfau, Albert T. Young, 2021, ArXiv.

Predicting new molecular targets for known drugs

Michael J. Keiser, J. Irwin, B. Shoichet, 2009, Nature.

Relating protein pharmacology by ligand chemistry

Michael J. Keiser, J. Irwin, B. Shoichet, 2007, Nature Biotechnology.

Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets

Michael J. Keiser, B. Shoichet, J. Jenkins, 2012, Nature.

The Chemical Basis of Pharmacology

Michael J. Keiser, J. Irwin, B. Shoichet, 2010, Biochemistry.

Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs.

Michael J. Keiser, B. Shoichet, A. Degraw, 2010, Journal of medicinal chemistry.

Off-target networks derived from ligand set similarity.

Michael J. Keiser, J. Hert, 2009, Methods in molecular biology.

ChemInform Abstract: Quantifying the Relationships among Drug Classes.

Tudor I. Oprea, Michael J. Keiser, T. Oprea, 2008 .

Systems pharmacology augments drug safety surveillance

Michael J. Keiser, S. Vilar, G. Hripcsak, 2014, Clinical pharmacology and therapeutics.

A simple representation of three-dimensional molecular structure

Michael J. Keiser, Elena L. Cáceres, B. Roth, 2017, bioRxiv.

A simple representation of three-dimensional molecular structure

Michael J. Keiser, Elena L. Cáceres, Xi-Ping Huang, 2017, bioRxiv.

Learning Molecular Representations for Medicinal Chemistry.

Michael J. Keiser, Kangway V. Chuang, Kangway V Chuang, 2020, Journal of medicinal chemistry.

Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors†

Michael J. Keiser, J. Irwin, B. Shoichet, 2010, Journal of medicinal chemistry.

Zebrafish behavioral profiling identifies multi-target antipsychotic-like compounds

Michael J. Keiser, Xi-Ping Huang, B. Roth, 2016, Nature chemical biology.

Quantifying Biogenic Bias in Screening Libraries

Jérôme Hert, Brian K. Shoichet, Michael J. Keiser, 2009, Nature chemical biology.

Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology.

Michael J. Keiser, Michael J Keiser, Leo Gendelev, 2016, ACS chemical biology.

Chemical Informatics and Target Identification in a Zebrafish Phenotypic Screen

Bryan L. Roth, Brian K. Shoichet, Michael J. Keiser, 2011, Nature chemical biology.

Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

Nhat Anh Cao, Maria L. Wei, Albert T. Young, 2021, npj Digital Medicine.

In Silico Molecular Comparisons of C. elegans and Mammalian Pharmacology Identify Distinct Targets That Regulate Feeding

Maria F. Sassano, Bryan L. Roth, Brian K. Shoichet, 2013, PLoS biology.

Prediction and validation of enzyme and transporter off-targets for metformin

Brian K. Shoichet, Michael J. Keiser, Youcai Zhang, 2015, Journal of Pharmacokinetics and Pharmacodynamics.

Differential methylation and expression of genes in the hypoxia-inducible factor 1 signaling pathway are associated with paclitaxel-induced peripheral neuropathy in breast cancer survivors and with preclinical models of chemotherapy-induced neuropathic pain

Michael J. Keiser, A. Olshen, M. Sirota, 2020, Molecular pain.

Deep learning from multiple experts improves identification of amyloid neuropathologies

Michael J. Keiser, Nicholas C. Mew, Daniel R. Wong, 2021, bioRxiv.

Validation of machine learning models to detect amyloid pathologies across institutions

Michael J. Keiser, D. Gutman, M. Gearing, 2020, Acta Neuropathologica Communications.

In Silico Prediction of Drug Side Effects

Michael J. Keiser, 2015 .

Prioritizing virtual screening with interpretable interaction fingerprints

Michael J. Keiser, Matthew J. O’Meara, R. S. Ferreira, 2022, bioRxiv.

In silico discovery of repetitive elements as key sequence determinants of 3D genome folding

Michael J. Keiser, K. Pollard, L. Gunsalus, 2022, bioRxiv.

A single-cell gene expression language model

Michael J. Keiser, Will Connell, Umair W Khan, 2022, ArXiv.

Predicting Cellular Drug Sensitivity using Conditional Modulation of Gene Expression

Michael J. Keiser, Will Connell, 2021, bioRxiv.

Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics

Michael J. Keiser, Daniel R. Wong, Jacob I. Ayers, 2021, Nature Machine Intelligence.

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information

Conrad C. Huang, Michael J. Keiser, Charlotte A. Nelson, 2023, Bioinformatics.

Zebrafish behavioural profiling identifies GABA and serotonin receptor ligands related to sedation and paradoxical excitation

Michael J. Keiser, S. Tomita, J. Sello, 2019, Nature Communications.

Artificial Intelligence in Dermatology: A Primer.

Maria L. Wei, Jacob Pfau, Albert T. Young, 2020, The Journal of investigative dermatology.

Evolutionarily Conserved Roles for Blood-Brain Barrier Xenobiotic Transporters in Endogenous Steroid Partitioning and Behavior.

Michael J. Keiser, H. Ishimoto, T. Kitamoto, 2017, Cell reports.

Evolutionarily conserved roles for blood-brain barrier xenobiotic transporters in endogenous steroid partitioning and behavior

Michael J. Keiser, H. Ishimoto, T. Kitamoto, 2017, bioRxiv.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels

Michael J. Keiser, Daniel R. Wong, C. DeCarli, 2023, bioRxiv.

Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Michael J. Keiser, Kangway V. Chuang, Kangway V Chuang, 2018, Science.

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Michael J. Keiser, Kangway V. Chuang, L. Beckett, 2018, bioRxiv.

The Presynaptic Component of the Serotonergic System is Required for Clozapine's Efficacy

Michael J. Keiser, Bryan L Roth, John J Irwin, 2011, Neuropsychopharmacology.

Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach.

Michael J. Keiser, P. Sullivan, J. Dudley, 2016, The lancet. Psychiatry.

Prediction and validation of enzyme and transporter off-targets for metformin

Michael J. Keiser, B. Shoichet, K. Giacomini, 2015, Journal of Pharmacokinetics and Pharmacodynamics.

A Biomedical Open Knowledge Network Harnesses the Power of AI to Understand Deep Human Biology

Michael J. Keiser, Charlotte A. Nelson, R. Pearce, 2022, AI Mag..

Deep learning from multiple experts improves identification of amyloid neuropathologies

Michael J. Keiser, Nicholas C. Mew, Daniel R. Wong, 2022, Acta Neuropathologica Communications.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels

Michael J. Keiser, Daniel R. Wong, C. DeCarli, 2023, Communications biology.

Validation of machine learning models to detect amyloid pathologies across institutions

Michael J. Keiser, D. Gutman, M. Gearing, 2020, Acta Neuropathologica Communications.

Adversarial Controls for Scientific Machine Learning.

Michael J. Keiser, Kangway V. Chuang, Kangway V Chuang, 2018, ACS chemical biology.

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

Michael J. Keiser, Michael J Keiser, Elena L Cáceres, 2020, Journal of chemical information and modeling.

Learning chemical sensitivity reveals mechanisms of cellular response

Michael J. Keiser, H. Goodarzi, Will Connell, 2023, bioRxiv.

383 Assessing deep learning artefact bias using global saliency

Maria L. Wei, Albert T. Young, Michael J. Keiser, 2020, Journal of Investigative Dermatology.

A pilot study of the pharmacodynamic impact of SSRI drug selection and beta-1 receptor genotype (ADRB1) on cardiac vital signs in depressed patients: a novel pharmacogenetic approach.

Michael J. Keiser, J. Bishop, V. Ellingrod, 2010, Psychopharmacology bulletin.

Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs.

Michael J. Keiser, B. Shoichet, A. Degraw, 2010, Journal of medicinal chemistry.

Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach.

Michael J. Keiser, P. Sullivan, J. Dudley, 2016, The lancet. Psychiatry.

Predicting new molecular targets for known drugs

Michael J. Keiser, J. Irwin, B. Shoichet, 2009, Nature.

Evolutionarily conserved roles for blood-brain barrier xenobiotic transporters in endogenous steroid partitioning and behavior

Michael J. Keiser, H. Ishimoto, T. Kitamoto, 2017, bioRxiv.

Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology.

Michael J. Keiser, Leo Gendelev, Matthew N. McCarroll, 2016, ACS chemical biology.