Prediction of protein secondary structure by the hidden Markov model

By Kiyoshi Asai, S. Hayamizu, Ken'ichi Handa. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jinbo Xu
2. Sheng Wang
3. Zhiyong Wang
4. Jianzhu Ma
5. Feng Zhao
6. Jian Peng
7. Z. Aydın
8. David Baker

Citations in the corpus

• Protein secondary structure prediction using periodic-quadratic-logistic models: statistical and theoretical issues (1994, unknown): “Virtually every conceivable computational technique in these fields (e.g., information theory [6, 12, 131, artificial neural networks [15, 20, 221, cascaded networks [18, 19, 273, hybrid systems [28], nearest neighbor methods [21], hidden markov chains [ 4 ], machine learning [17, 251, mutual inform”
• HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. (2000, background): “…junctions (Kulp et al., 1996, Burge & Karlin, 1998, Lukashin & Borodovsky, 1998, Henderson et al., 1997), ®nding transmembrane regions in proteins (Sonnhammer et al., 1998), and secondary structure prediction (Asai et al., 1993, Stultz et al., 1993; Di Francesco et al., 1997, Lio et al., 1998).”
• Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011, methodology): “In the ensuing 22 years, dozens of methods have been proposed for improving upon this baseline, with significant advances achieved by exploiting homologs of the query sequence [2] and by employing methods, such as hidden Markov models, which exploit patterns in the protein sequence [3].”
• Protein 8-class secondary structure prediction using conditional neural fields (2010, methodology): “Hidden Markov model (HMM) [18] is capable of describing this relationship [19, 20], but it is challenging for HMM to model the complex nonlinear relationship between input protein features and SS, especially when a large amount of heterogeneous protein features are available.”
• 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 ].”
• Profile hidden Markov models (1998, background): “…1997; Lukashin and Borodovsky, 1998), radiation hybrid mapping (Slonim et al., 1997), genetic linkage mapping (Kruglyak et al., 1996), phylogenetic analysis (Felsenstein and Churchill, 1996; Thorne et al., 1996) and protein secondary structure prediction (Asai et al., 1993; Goldman et al., 1996).”
• Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, methodology): “Hidden Markov model (HMM) (Rabiner, 1989) is capable of describing this relationship (Asai et al, 1993; Aydin et al 2006), but it is challenging for HMM to model the complex nonlinear relationship between input protein features and SS, especially when a large amount of heterogeneous protein…”