Search Results - Faissol, Daniel

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  1. 1
    Large-scale application of free energy perturbation calculations for antibody design

    Large-scale application of free energy perturbation calculations for antibody design by Zhu, Fangqiang, Bourguet, Feliza A., Bennett, William F. D., Lau, Edmond Y., Arrildt, Kathryn T., Segelke, Brent W., Zemla, Adam T., Desautels, Thomas A., Faissol, Daniel M.

    Published in Scientific reports
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  2. 2
    Computationally restoring the potency of a clinical antibody against Omicron

    Computationally restoring the potency of a clinical antibody against Omicron by Desautels, Thomas A., Arrildt, Kathryn T., Zemla, Adam T., Lau, Edmond Y., Zhu, Fangqiang, Ricci, Dante, Cronin, Stephanie, Zost, Seth J., Binshtein, Elad, Scheaffer, Suzanne M., Dadonaite, Bernadeta, Petersen, Brenden K., Engdahl, Taylor B., Chen, Elaine, Handal, Laura S., Hall, Lynn, Goforth, John W., Vashchenko, Denis, Nguyen, Sam, Weilhammer, Dina R., Lo, Jacky Kai-Yin, Rubinfeld, Bonnee, Saada, Edwin A., Weisenberger, Tracy, Lee, Tek-Hyung, Whitener, Bradley, Case, James B., Ladd, Alexander, Silva, Mary S., Haluska, Rebecca M., Grzesiak, Emilia A., Earnhart, Christopher G., Hopkins, Svetlana, Bates, Thomas W., Thackray, Larissa B., Segelke, Brent W., Lillo, Antonietta Maria, Sundaram, Shivshankar, Bloom, Jesse D., Diamond, Michael S., Crowe, James E., Carnahan, Robert H., Faissol, Daniel M.

    Published in Nature (London)
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  3. 3
    Timing of testing and treatment for asymptomatic diseases

    Timing of testing and treatment for asymptomatic diseases by Kırkızlar, Eser, Faissol, Daniel M., Griffin, Paul M., Swann, Julie L.

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  4. 4
    Bias in Markov models of disease

    Bias in Markov models of disease by Faissol, Daniel M., Griffin, Paul M., Swann, Julie L.

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  5. 5
    Language model-accelerated deep symbolic optimization

    Language model-accelerated deep symbolic optimization by da Silva, Felipe Leno, Goncalves, Andre, Nguyen, Sam, Vashchenko, Denis, Glatt, Ruben, Desautels, Thomas, Landajuela, Mikel, Faissol, Daniel, Petersen, Brenden

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  6. 6
    Abstract B008: Man vs machine: Crowdsourcing vs deep learning of adaptive therapy strategies

    Abstract B008: Man vs machine: Crowdsourcing vs deep learning of adaptive therapy strategies by Bravo, Rafael R., Peterson, Brenden K., Robertson-Tessi, Mark, Faissol, Daniel M., Anderson, Alexander R.A.

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  7. 7
    Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

    Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants by Desautels, Thomas A, Arrildt, Kathryn T, Zemla, Adam T, Lau, Edmond Y, Zhu, Fangqiang, Ricci, Dante, Cronin, Stephanie, Zost, Seth, Binshtein, Elad, Scheaffer, Suzanne M, Engdahl, Taylor B, Chen, Elaine, Goforth, John W, Vashchenko, Denis, Nguyen, Sam, Weilhammer, Dina R, Lo, Jacky Kai-Yin, Rubinfeld, Bonnee, Saada, Edwin A, Weisenberger, Tracy, Lee, Tek-Hyung, Whitener, Bradley, Case, James B, Ladd, Alexander, Silva, Mary S, Haluska, Rebecca M, Grzesiak, Emilia A, Bates, Thomas W, Petersen, Brenden K, Thackray, Larissa B, Segelke, Brent W, Lillo, Antonietta Maria, Sundaram, Shankar, Diamond, Michael S, Crowe, James E, Carnahan, Robert H, Faissol, Daniel M

    Published in bioRxiv
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  8. 8
    Exploitation of Ambiguous Cues to Infer Terrorist Activity

    Exploitation of Ambiguous Cues to Infer Terrorist Activity by Ni, Kevin S., Faissol, Daniel, Edmunds, Thomas, Wheeler, Richard

    Published in Decision analysis
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  9. 9
    Exploitation of Ambiguous Cues to Infer Terrorist Activity

    Exploitation of Ambiguous Cues to Infer Terrorist Activity by Ni, Kevin S, Faissol, Daniel, Edmunds, Thomas, Wheeler, Richard

    Published in Decision analysis
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  10. 10
    Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

    Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement by Yang, Jiachen, Mittal, Ketan, Dzanic, Tarik, Petrides, Socratis, Keith, Brendan, Petersen, Brenden, Faissol, Daniel, Anderson, Robert

    Published in arXiv.org
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  11. 11
    Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing

    Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing by Desautels, Thomas, Zemla, Adam, Lau, Edmond, Franco, Magdalena, Faissol, Daniel

    Published in bioRxiv
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  12. 12
    Single Episode Policy Transfer in Reinforcement Learning

    Single Episode Policy Transfer in Reinforcement Learning by Yang, Jiachen, Petersen, Brenden, Zha, Hongyuan, Faissol, Daniel

    Published in arXiv.org
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  13. 13
    Symbolic Regression via Neural-Guided Genetic Programming Population Seeding

    Symbolic Regression via Neural-Guided Genetic Programming Population Seeding by Mundhenk, T Nathan, Landajuela, Mikel, Glatt, Ruben, Santiago, Claudio P, Faissol, Daniel M, Petersen, Brenden K

    Published in arXiv.org
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  14. 14
    Reinforcement Learning for Adaptive Mesh Refinement

    Reinforcement Learning for Adaptive Mesh Refinement by Yang, Jiachen, Dzanic, Tarik, Petersen, Brenden, Kudo, Jun, Mittal, Ketan, Tomov, Vladimir, Jean-Sylvain Camier, Zhao, Tuo, Zha, Hongyuan, Kolev, Tzanio, Anderson, Robert, Faissol, Daniel

    Published in arXiv.org
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  15. 15
    Improving exploration in policy gradient search: Application to symbolic optimization

    Improving exploration in policy gradient search: Application to symbolic optimization by Landajuela, Mikel, Petersen, Brenden K, Kim, Soo K, Santiago, Claudio P, Glatt, Ruben, Mundhenk, T Nathan, Pettit, Jacob F, Faissol, Daniel M

    Published in arXiv.org
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  16. 16
    Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis

    Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis by Petersen, Brenden K, Yang, Jiachen, Grathwohl, Will S, Cockrell, Chase, Santiago, Claudio, An, Gary, Faissol, Daniel M

    Published in arXiv.org
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  17. 17
    SARS-COV-2 Omicron variant predicted to exhibit higher affinity to ACE-2 receptor and lower affinity to a larger range of neutralizing antibodies, using a rapid assessment platform

    SARS-COV-2 Omicron variant predicted to exhibit higher affinity to ACE-2 receptor and lower affinity to a larger range of neutralizing antibodies, using a rapid assessment platform by Zemla, Adam, Desautels, Thomas, Lau, Edmond, Zhu, Fangqiang, Arrildt, Kathryn, Segelke, Brent, Sundaram, Shankar, Daniel Mello Faissol

    Published in bioRxiv
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