PUBLICATIONS

All | Autonomous Driving | Computational Structural Biology | Computational Systems Biology | Deep Learning | Deformable Object Manipulation | Differentiable Robot Algorithms | Geometric Computation | Human-Robot Interaction | Integrating Planning and Learning | Inverse Reinforcement Learning | Kinodynamic Motion Planning | Motion Planning | Motion Planning Applications | Object Manipulation | Planning under Uncertainty | POMDP Applications | POMDP Planning | Probabilistic Motion Planning | Reinforcement Learning | Robot Navigation | Sampling Strategies for PRM Planning | Target Tracking | Trust-Aware Decision Making

B. Gipson, D. Hsu, L.E. Kavraki, and J.C. Latombe. Computational models of protein kinematics and dynamics: Beyond simulation. Annu. Rev. Anal. Chem, 5:273–291, 2012. DOI: 10.1146/annurev-anchem-062011-143024.
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T.-H. Chiang, D. Hsu, and J.C. Latombe. Markov dynamic models for long-timescale protein motion. Bioinformatics, 26(12):i269–i277, 2010. Special issue on Int. Conf. on Intelligent Systems for Molecular Biology (ISMB) 2010.
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A. Nigham, L. Tucker-Kellogg, I. Mihalek, C. Verma, and D. Hsu. pFlexAna: Detecting conformational changes in remotely related proteins. Nucleic Acids Res., 36:W246–W251, 2008. DOI: 10.1093/nar/gkn259.
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A. Nigham and D. Hsu. Protein conformational flexibility analysis with noisy data. J. Computational Biology, 15(7):813–828, 2008.
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A. Nigham and D. Hsu. Protein conformational flexibility analysis with noisy data. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), pp. 396–411, 2007.
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T.-H. Chiang, M.S. Apaydin, D.L. Brutlag, D. Hsu, and J.C. Latombe. Using stochastic roadmap simulation to predict experimental quantities in protein folding kinetics: Folding rates and phi-values. J. Computational Biology, 14(5):578–593, 2007.
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T.-H. Chiang, M.S. Apaydin, D.L. Brutlag, D. Hsu, and J.C. Latombe. Predicting experimental quantities in protein folding kinetics using stochastic roadmap simulation. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), pp. 410–424, Springer, 2006.
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M.S. Apaydin, D.L. Brutlag, C. Guestrin, D. Hsu, J.C. Latombe, and C. Varma. Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion. J. Computational Biology, 10(3-4):247–281, 2003.
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D. Brutlag, M.S. Apaydin, C. Guestrin, D. Hsu, C. Varma, A. Singh, and J.-C. Latombe. Using robotics to fold proteins and dock ligands. Bioinformatics, 18:S74–S83, 2002.
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R.-P. Berretty, D. Hsu, L. Kettner, A. Mascarenhas, M.R. Redinbo, and J. Snoeyink. Ligand binding to the pregnane X receptor by geometric matching of hydrogen bonding. In L. Florea and others, editors, Currents in Computational Molecular Biology, pp. 22–23, 2002. The booklet contains extended poster abstracts from RECOMB 2002.
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M.S. Apaydin, D.L. Brutlag, C. Guestrin, D. Hsu, and J.C. Latombe. Stochastic conformational roadmaps for computing ensemble properties of molecular motion. In J.-D. Boissonnat and others, editors, Algorithmic Foundations of Robotics V—Proc. Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 131–148, Springer, 2002.
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M.S. Apaydin, D.L. Brutlag, C. Guestrin, D. Hsu, and J.C. Latombe. Stochastic roadmap simulation: An efficient representation and algorithm for analyzing molecular motion. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), pp. 12–21, 2002.
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