Learning to Optimize via Posterior Sampling von Russo, Daniel, Van Roy, Benjamin

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Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets von Korotcov, Alexandru, Tkachenko, Valery, Russo, Daniel P, Ekins, Sean

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Exploiting machine learning for end-to-end drug discovery and development von Ekins, Sean, Puhl, Ana C., Zorn, Kimberley M., Lane, Thomas R., Russo, Daniel P., Klein, Jennifer J., Hickey, Anthony J., Clark, Alex M.

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Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling von Wang, Wenyi, Sedykh, Alexander, Sun, Hainan, Zhao, Linlin, Russo, Daniel P, Zhou, Hongyu, Yan, Bing, Zhu, Hao

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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 von Ciallella, Heather L, Russo, Daniel P, Aleksunes, Lauren M, Grimm, Fabian A, Zhu, Hao

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Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction von Russo, Daniel P, Zorn, Kimberley M, Clark, Alex M, Zhu, Hao, Ekins, Sean

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Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay von Jia, Xuelian, Wen, Xia, Russo, Daniel P., Aleksunes, Lauren M., Zhu, Hao

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Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties von Russo, Daniel P., Yan, Xiliang, Shende, Sunil, Huang, Heng, Yan, Bing, Zhu, Hao

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Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across von Russo, Daniel P, Strickland, Judy, Karmaus, Agnes L, Wang, Wenyi, Shende, Sunil, Hartung, Thomas, Aleksunes, Lauren M, Zhu, Hao

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An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits von Wang, Tong, Russo, Daniel P., Demokritou, Philip, Jia, Xuelian, Huang, Heng, Yang, Xinyu, Zhu, Hao

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Integrating structure annotation and machine learning approaches to develop graphene toxicity models von Wang, Tong, Russo, Daniel P., Bitounis, Dimitrios, Demokritou, Philip, Jia, Xuelian, Huang, Heng, Zhu, Hao

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Simple Bayesian Algorithms for Best-Arm Identification von Russo, Daniel

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Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data von Ciallella, Heather L, Russo, Daniel P, Sharma, Swati, Li, Yafan, Sloter, Eddie, Sweet, Len, Huang, Heng, Zhu, Hao

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Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches von Ciallella, Heather L., Russo, Daniel P., Aleksunes, Lauren M., Grimm, Fabian A., Zhu, Hao

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Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways von Russo, Daniel P., Aleksunes, Lauren M., Goyak, Katy, Qian, Hua, Zhu, Hao

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Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data von Zhao, Linlin, Russo, Daniel P, Wang, Wenyi, Aleksunes, Lauren M, Zhu, Hao

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Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images von Yan, Xiliang, Zhang, Jin, Russo, Daniel P, Zhu, Hao, Yan, Bing

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The effect of race in head and neck cancer: A meta-analysis controlling for socioeconomic status von Russo, Daniel P., Tham, Tristan, Bardash, Yonatan, Kraus, Dennis

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Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets von Zorn, Kimberley M, Lane, Thomas R, Russo, Daniel P, Clark, Alex M, Makarov, Vadim, Ekins, Sean

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How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage von Russo, Daniel, Zou, James

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