Approximate Inference and Protein-Folding

By Chen Yanover, Yair Weiss. 2002

135 citations recorded by the source corpus.

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, background): “What makes DPPs particularly unique is the marriage of these computational advantages with the ability to express global, negative interactions between modeling variables; this repulsive domain is notoriously intractable using traditional approaches like graphical models [17, 70, 82, 109, 146, 156, ”
Kernel Belief Propagation (2011, methodology): “Belief propagation is an inference algorithm for graphical models that has been widely and successfully applied in a great variety of domains, including vision (Sudderth et al., 2003), protein folding (Yanover & Weiss, 2002), and turbo decoding (McEliece et al., 1998).”
Linear Programming Relaxations and Belief Propagation - An Empirical Study (2006, methodology): “The task of finding the most probable assignment (or MAP) in a graphical model c mes up in a wide range of applications including image understanding (Tappen and Freeman, 2003), error correcting codes (Feldman et al., 2003) and protein folding (Yanover and Weiss, 2002).”
Protein sequence design with a learned potential (2020, methodology): “Methods for optimizing over residues and rotamers include variations on the dead-end elimination (DEE) algorithm [32, 33, 34, 35], integer linear programming [36, 37], belief propagation [38, 39], and Markov Chain Monte Carlo (MCMC) with simulated annealing [40, 41, 42].”
MODELING AND INFERENCE OF SEQUENCE-STRUCTURE SPECIFICITY (2009, methodology): “We [10] and others [26] have previously developed probabilistic graphical models to encode all such conditional independencies between side-chain variables, given the sequence and backbone conformation (blue subgraph in Fig.”
Collective Classi!cation in Network Data (2008, result): “Our difficulty with MF and LBP is consistent with previous work (Weiss 2001, Mooij and Kappen 2004, Yanover and Weiss 2002) and should be taken into consideration when choosing to apply these algorithms.”
Residual Splash for Optimally Parallelizing Belief Propagation (2009, methodology): “Nonetheless, BP on " loopy " graphs (often referred to as Loopy BP ) is used extensively with great success as an approximate inference algorithm [McEliece et al., 1998, Sun et al., 2003, Yedidia et al., 2003, Yanover and Weiss, 2002, Yanover et al., 2007].”
Collective Classification in Network Data (2008, result): “Our difficulty with MF and LBP are consistent with previous work [61, 43, 64] and should be taken into consideration when choosing to apply these algorithms.”
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing (2006, methodology): “We consider examples from two markedly different models in computational biology (Jaimovich et al., 2005; Yanover and Weiss, 2003).”