title: Protein secondary structure prediction with partially recurrent neural networks.
abstract: Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures alpha-helix, beta-sheet, and "coil" for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 x 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.
authors.affiliation: Department of Molecular Biophysics, German Cancer Research Center, Heidelberg, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Reczko
mesh_terms: D000595:Amino Acid Sequence; D010450:Endopeptidases / Q000737:chemistry* / Q000235:genetics; D006454:Hemoglobins / Q000737:chemistry* / Q000235:genetics; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D017386:Sequence Homology, Amino Acid
chemical_list: D006454:Hemoglobins; D010450:Endopeptidases