Improvements in protein secondary structure prediction by an enhanced neural network.

By D. Kneller, Fred E. Cohen, R. Langridge. 1990

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Citations in the corpus

Support vector machines : theory and applications (2005, background): “Algorithms of this generation include statistical information [36, 40], sequence patterns [41, 42], multi-layer networks [43, 44, 45, 49], multivariate statistics [46], nearest-neighbor algorithms [47], etc.”
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures (2018, background): “for the development of prediction methods, and indeed as early as the mid 70s secondary structure prediction (SSP) has gained much interest and was tackled by a wide variety of statistical approaches [5, 12, 8, 15, 13, 20]. To ease the prediction challenge, these studies typically merged the eight D”
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes (2005, methodology): “For example, the amino acid composition is a typical discrete form that has been widely used in predicting protein structural class (Bahar et al., 1997; Cai et al., 2000; Chandonia & Karplus, 1995; Chou & Zhang, 1993; Chou & Maggiora, 1998; Chou & Zhang, 1994; Chou, 1989; Deleage & Roux, 1987; Klein”
Bioinformatics - a practical guide to the analysis of genes and proteins (2001, background): “Keep in mind that, in practice, these are not necessarily treated as two separate parts—for instance, one’s service provider may also be the same company that will run cables or fibers right into one’s home or office.”
Cascaded multiple classifiers for secondary structure prediction (2000, unknown): “…either on neural networks exploiting patterns of residues and/or physico-chemical properties (Quian and Sejnowski, 1988; Holley and Karplus, 1989; Kneller et al , 1990; Rost and Sander, 1993; Kawabata and Doi, 1997) or on k-nearest-neighbor methods (Biou et al , 1988; Zang et al , 1992, Yi and…”
A simple and fast approach to prediction of protein secondary structure from multiply aligned sequences with accuracy above 70% (1995, background): “…neural networks (cf. Qian & Sejnowski, 1988; Holley & Karplus, 1989; Zhang et al., 1992) have resulted in 63-67% correctness for single sequence prediction, and enhanced neural network procedures (Kneller et al., 1990) have achieved 79% if the query protein is known to contain largely a-helices.”
Adaptive computation and machine learning (1998, methodology): “Most of the subsequent work on predicting secondary structure using NNs [78, 262, 323, 505, 451, 452, 290, 427] has been based on the architecture described above, sometimes in combination with other methods [582, 377] such as the Chou-Fasman rules [129].”
Protein 8-class secondary structure prediction using conditional neural fields (2010, methodology): “Our CNF model performs best on the proteins with Neff values in [7,8).”
Review: protein secondary structure prediction continues to rise. (2001, background): “k Percentage of proteins correctly sorted into one of the four classes: all-alpha (length . 60, helix .45%, strand ,5%), all-beta (length . 60, helix ,5%, strand .45%), alpha/beta (length . 60, helix .30%, strand .20%), other (thresholds for classification from (122, 125 , 126).”
A novel influenza A virus mitochondrial protein that induces cell death (2001, methodology): “According to the NNPREDICT algorithm, PB1-F2 has a propensity to form an amphipathic helix extending from Leu 69 to Phe 83 (ref”