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Learning to Optimize via Posterior Sampling

Mathematics of operations research, 2014-11, Vol.39 (4), p.1221-1243

2014

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Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Molecular pharmaceutics, 2017-12, Vol.14 (12), p.4462-4475

2017

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Exploiting machine learning for end-to-end drug discovery and development

Nature materials, 2019-05, Vol.18 (5), p.435-441

2019

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Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling

ACS nano, 2017-12, Vol.11 (12), p.12641-12649

2017

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Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Environmental science & technology, 2021-08, Vol.55 (15), p.10875-10887

2021

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Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Molecular pharmaceutics, 2018-10, Vol.15 (10), p.4361-4370

2018

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Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Journal of hazardous materials, 2022-08, Vol.436, p.129193-129193, Article 129193

2022

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Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties

Analytical chemistry (Washington), 2020-10, Vol.92 (20), p.13971-13979

2020

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Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Environmental health perspectives, 2019-04, Vol.127 (4), p.47001

2019

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Integrating structure annotation and machine learning approaches to develop graphene toxicity models

Carbon (New York), 2023-02, Vol.204, p.484-494

2023

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Simple Bayesian Algorithms for Best-Arm Identification

Operations research, 2020-11, Vol.68 (6), p.1625-1647

Russo, Daniel

2020

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Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways

Environmental science & technology, 2023-08, Vol.57 (33), p.12291-12301

2023

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Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data

Toxicological sciences, 2020-04, Vol.174 (2), p.178-188

2020

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Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

Environmental science & technology, 2022-05, Vol.56 (9), p.5984-5998

2022

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Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

Laboratory investigation, 2021-04, Vol.101 (4), p.490-502

2021

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Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images

ACS sustainable chemistry & engineering, 2020-12, Vol.8 (51), p.19096-19104

2020

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Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

Molecular pharmaceutics, 2019-04, Vol.16 (4), p.1620-1632

2019

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Predictive Modeling of Angiotensin I‑Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches

Journal of agricultural and food chemistry, 2020-10, Vol.68 (43), p.12132-12140

2020

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The effect of race in head and neck cancer: A meta-analysis controlling for socioeconomic status

American journal of otolaryngology, 2020-11, Vol.41 (6), p.102624-102624, Article 102624

2020

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Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data

ALTEX, alternatives to animal experimentation, 2016, Vol.33 (2), p.123-134

2016

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How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage

IEEE transactions on information theory, 2020-01, Vol.66 (1), p.302-323

2020

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IEEE_Xplore

Approximation Benefits of Policy Gradient Methods with Aggregated States

Management science, 2023-11, Vol.69 (11), p.6898-6911

Russo, Daniel

2023

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Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction

Environmental science & technology, 2020-10, Vol.54 (19), p.12202-12213

2020

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Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Molecular pharmaceutics, 2018-10, Vol.15 (10), p.4346-4360

2018

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Journal of empirical legal studies, 2022-03, Vol.19 (1), p.250-292

2022

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Learning to Optimize via Posterior Sampling

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Exploiting machine learning for end-to-end drug discovery and development

Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Integrating structure annotation and machine learning approaches to develop graphene toxicity models

Simple Bayesian Algorithms for Best-Arm Identification

Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways

Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

Predictive Modeling of Angiotensin I‑Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches

The effect of race in head and neck cancer: A meta-analysis controlling for socioeconomic status

Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data

How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage

Approximation Benefits of Policy Gradient Methods with Aggregated States

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

U.S. Copyright Termination Notices 1977–2020: Introducing New Datasets

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