Protein secondary structure prediction with a neural network.

By L. Howard, H. and, Martin Karplus. 1989

How researchers see this paper

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

Citations in the corpus

Cascaded multiple classifiers for secondary structure prediction (2000, unknown): “…which are often based 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;…”
Neural networks for secondary structure and structural class predictions (1995, methodology): “As before, these results were compiled on the Holley and Karplus (1989) training and test sets for ease in com- Neural nets for secondary structure and class prediction 28 1 a A neural network was trained on the 48-protein training set and accuracy was measured using the 14-protein test set.”
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”
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].”
A simple and fast approach to prediction of protein secondary structure from multiply aligned sequences with accuracy above 70% (1995, background): “The use of single three-layered 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…”
Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, methodology): “The second-generation methods, represented by the GarnierOsguthorpe-Robson (GOR) method [66] and the Lim method [67], used a sliding window of neighbouring residues and various theoretical algorithms such as statistical information [66, 68, 69], graph theory [70], neural networks [71, 72], logic-bas”
Protein 8-class secondary structure prediction using conditional neural fields (2010, unknown): “[1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,9) [9,20) 0.”
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features (2004, methodology): “For this domain , PEBLS included a post - processing algorithm based on the minimal sequence length restrictions used by Holley and Karplus ( 1989 ) .”
Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, methodology): “19 prediction (Qian and Sejnowski, 1988; Holley and Karplus, 1989; Kneller and Cohen, 1990; Rost and Sander, 1993; Rost and Sander, 1994; Rost 1996; Cuff and Barton 1999); these methods achieve Q3 accuracy of approximately 80%.”
Deep learning methods in protein structure prediction (2020, methodology): “In a second generation of predictors [54], information from more than one AA at a time was fed to various methods, including FFNN to predict secondary structure [38,39], and least squares, i.e. a standard regression analysis, to predict hydrophobicity values [55].”