Protein secondary structure prediction using periodic-quadratic-logistic models: statistical and theoretical issues

By Peter J. Munson, V. D. Francesco, R. Porrelli. 1994

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Citations in the corpus

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (2014, methodology): “…dependencies us-ing bidirectional recurrent neural networks (BRNN) (Baldi et al., 1999; Pollastri et al., 2002), probabilistic graphical models (Schmidler et al., 2000; Chu et al., 2004; van der Maaten et al., 2011), or combination of neural network and graphical models, like conditional…”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, methodology): “Previously, Schmidler et al. (2000) used the Bayesian model-averaging approach to estimate probabilities of a particular secondary structure after averaging over all possible segmentation.”
Explicit-Duration Markov Switching Models (2014, background): “…financial time series analysis [Bulla and Bulla, 2006], rainfall time series analysis [Sansom and Thomson, 2001], protein structure segmentation [Schmidler et al., 2000], gene finding [Winters-Hilt et al., 2010], DNA analysis [Barbu and Limnios, 2008, Fearnhead and Vasileiou, 2009], plant…”
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, methodology): “Results and discussion We first compared the performances of BSPSS [14] and IPSSP in strict jacknife conditions.”
A graphical model for protein secondary structure prediction (2004, methodology): “Schmidler et al. (2000) proposed a helical segment model with lookup tables to capture helical capping signals and the hydrophobicity dependency of segmental residues.”
Hidden semi-Markov models (2010, unknown): “…attentions from vast application areas such as change-point/end-point detection for semi-conductor manufacturing [64], protein structure prediction [162], mobility tracking in cellular networks [197], analysis of branching and ﬂowering patterns in plants [69], rain events time series model [159],…”
Review: protein secondary structure prediction continues to rise. (2001, background): “(iv) Schmidler et al. ( 65 ) use a simple statistical model; the novel aspect is to replace compiling statistics over fixed stretches of N residues by segments signifying regular secondary structure (helix, strand).”
Bayesian Methods for Hidden Markov Models (2002, background): “Hidden semi-Markov models have been accommo-dated by running forward-backward recursions conditional on the vector of durations w D 4w 1 1w 2 1 : : : 5 and then marginal-izing over w (Schmidler, Liu, and Brutlag 2000, 2001; Ge and Smyth 2000).”