HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins.

By C. Bystroff, Vesteinn Thorsson, David Baker. Journal of Molecular Biology, 2000

348 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/934ecc62c63ac053db9a99074d34f0e3550f8d21

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Citations in the corpus

SAM-T08, HMM-based protein structure prediction (2009, background): “Bystroff,C., Thorsson,V. and Baker,D. (2000) HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins.”
An HMM posterior decoder for sequence feature prediction that includes homology information (2005, methodology): “, 1994b; Burge and Karlin, 1997), secondary structure predictors (Bystroff et al., 2000), sequence alignment programs (Needleman and Wunsch, 1970) and tools for sequence homology detection (Krogh et al.”
DATA MINING TECHNIQUES (2003, methodology): “Here, we use a HMM called HMMSTR [18] to derive these additional features.”
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011, methodology): “Indeed, the hidden Markov model, which has been used extensively to model protein sequences [7-11], is a very simple example of a DBN.”
Deep learning methods in protein structure prediction (2020, methodology): “…adopted for PSA prediction since the advent of the third generation of predictors [11], alongside more classic Machine Learning methods such as k-Nearest Neighbors [63,64], Linear Regression [65], Hidden Markov Models [66], Support Vector Machines (SVM) [67] and Support Vector Regression [68].”
Ab initio protein structure prediction. (2002, methodology): “Recent improvements to this program included the use of secondary structure prediction to bias the selection of fragments and terms to favor the assembly of strands into sheets and the burial of hydrophobic residues [23,27].”
Combining prediction of secondary structure and solvent accessibility in proteins (2005, background): “10 For a general overview of progress in this ﬁeld, the reader is referred to recent reviews.”
Review: protein secondary structure prediction continues to rise. (2001, background): “09. Eyrich, V. A., Standley, D. M., and Friesner, R. A. (1999) Prediction of protein tertiary structure to low resolution: Performance for a large and structurally diverse test set, J.”
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs) (2006, positive): “One of the earliest approaches to this problem has been applying simple hidden Markov models (HMMs) to protein secondary structure prediction and protein motif detection (Karplus et al., 1998; Durbin et al., 1998; Bystroff et al., 2000 ); Delcher et al. (1993) introduced probabilistic causal networ”
A simple and fast secondary structure prediction method using hidden neural networks (2005, neutral): “(Bystroff et al., 2000).”
ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction (2008, background): “The popular training techniques include neural networks (NN) [5,14], support vector machines (SVM) [14,15] and hidden Markov models (HMM) [7,11].”
Local Protein Structures (2007, methodology): “The same group also built a hidden Markov model called HMMSTR for protein sequences based on I-sites library [336].”
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, background): “Algorithms of protein secondary structure prediction frequently employ neural networks [1-7], support vector machines [8-13] and hidden Markov models [14-16].”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “, 1997; Sippl, 1993), and even protein conformation sampling (Bystroff et al., 2000; Simons et al., 1997; Zhao et al., 2008, 2010).”