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• W2147351132 abstract "Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%–80.2% (77.9%–80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles made by position-specific scoring matrices as input, while at the output level it predicts on three consecutive residues simultaneously. The predictions arise from tenfold, cross validated training and testing of 1032 protein sequences, using a scheme with primary structure neural networks followed by structure filtering neural networks. With respect to blind prediction, this work is preliminary and awaits evaluation by CASP4. Proteins 2000;41:17–20. © 2000 Wiley-Liss, Inc." @default.
• W2147351132 created "2016-06-24" @default.
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• W2147351132 date "2000-01-01" @default.
• W2147351132 modified "2023-10-15" @default.
• W2147351132 title "Prediction of protein secondary structure at 80% accuracy" @default.
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• W2147351132 doi "https://doi.org/10.1002/1097-0134(20001001)41:1<17::aid-prot40>3.0.co;2-f" @default.
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