Minimizing and Learning Energy Functions for Side-Chain Prediction
暂无分享,去创建一个
[1] R. Goldstein. Efficient rotamer elimination applied to protein side-chains and related spin glasses. , 1994, Biophysical journal.
[2] Aviezri S. Fraenkel. Protein folding, spin glass and computational complexity , 1997, DNA Based Computers.
[3] Mark W. Schmidt,et al. Accelerated training of conditional random fields with stochastic gradient methods , 2006, ICML.
[4] Yair Weiss,et al. Approximate Inference and Protein-Folding , 2002, NIPS.
[5] Jack Snoeyink,et al. An Adaptive Dynamic Programming Algorithm for the Side Chain Placement Problem , 2004, Pacific Symposium on Biocomputing.
[6] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[7] Yann LeCun,et al. Loss Functions for Discriminative Training of Energy-Based Models , 2005, AISTATS.
[8] M. Karplus,et al. Effective energy function for proteins in solution , 1999, Proteins.
[9] D. Baker,et al. Native protein sequences are close to optimal for their structures. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[10] Johan Desmet,et al. The dead-end elimination theorem and its use in protein side-chain positioning , 1992, Nature.
[11] Trevor Darrell,et al. Conditional Random Fields for Object Recognition , 2004, NIPS.
[12] D. Baker,et al. An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. , 2003, Journal of molecular biology.
[13] Martin J. Wainwright,et al. On the Optimality of Tree-reweighted Max-product Message-passing , 2005, UAI.
[14] Barry Honig,et al. Extending the accuracy limits of prediction for side-chain conformations. , 2001 .
[15] Z. Xiang,et al. Extending the accuracy limits of prediction for side-chain conformations. , 2001, Journal of molecular biology.
[16] Yair Weiss,et al. Globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[17] D. Baker,et al. Modeling structurally variable regions in homologous proteins with rosetta , 2004, Proteins.
[18] Vladimir Kolmogorov,et al. Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] A R Leach,et al. Exploring the conformational space of protein side chains using dead‐end elimination and the A* algorithm , 1998, Proteins.
[20] Adrian A Canutescu,et al. A graph‐theory algorithm for rapid protein side‐chain prediction , 2003, Protein science : a publication of the Protein Society.
[21] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[22] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[23] S. L. Mayo,et al. Conformational splitting: A more powerful criterion for dead‐end elimination , 2000 .
[24] A Joshua Wand,et al. Improved side‐chain prediction accuracy using an ab initio potential energy function and a very large rotamer library , 2004, Protein science : a publication of the Protein Society.
[25] S. Subbiah,et al. Prediction of protein side-chain conformation by packing optimization. , 1991, Journal of molecular biology.
[26] Mona Singh,et al. Solving and analyzing side-chain positioning problems using linear and integer programming , 2005, Bioinform..
[27] Yi Liu,et al. RosettaDesign server for protein design , 2006, Nucleic Acids Res..
[28] Stephen L. Mayo,et al. Conformational splitting: A more powerful criterion for dead-end elimination , 2000, J. Comput. Chem..
[29] William T. Freeman,et al. Understanding belief propagation and its generalizations , 2003 .
[30] Martin J. Wainwright,et al. MAP estimation via agreement on (hyper)trees: Message-passing and linear programming , 2005, ArXiv.
[31] Yair Weiss,et al. Linear Programming Relaxations and Belief Propagation - An Empirical Study , 2006, J. Mach. Learn. Res..
[32] Xiaojin Zhu,et al. Kernel conditional random fields: representation and clique selection , 2004, ICML.
[33] D. Baker,et al. High-resolution Structural and Thermodynamic Analysis of Extreme Stabilization of Human Procarboxypeptidase by Computational Protein Design , 2007, Journal of molecular biology.
[34] Hao Chen,et al. Beyond the rotamer library: Genetic algorithm combined with the disturbing mutation process for upbuilding protein side‐chains , 2003, Proteins.
[35] Jeffrey J. Gray,et al. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. , 2003, Journal of molecular biology.
[36] Roland L. Dunbrack,et al. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. , 1993, Journal of molecular biology.
[37] Alex Acero,et al. Hidden conditional random fields for phone classification , 2005, INTERSPEECH.
[38] P. Koehl,et al. A self consistent mean field approach to simultaneous gap closure and side-chain positioning in homology modelling , 1995, Nature Structural Biology.
[39] Martin J. Wainwright,et al. MAP estimation via agreement on trees: message-passing and linear programming , 2005, IEEE Transactions on Information Theory.
[40] A. Hasman,et al. Probabilistic reasoning in intelligent systems: Networks of plausible inference , 1991 .
[41] O. Schueler‐Furman,et al. Improved side‐chain modeling for protein–protein docking , 2005, Protein science : a publication of the Protein Society.