Protein secondary structure prediction with a neural network. Export

@article{citeulike:4258762, abstract = {A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum overall predictive accuracy of 63\% for three states: helix, sheet, and coil. A numerical measure of helix and sheet tendency for each residue was obtained from the calculations. When predictions were filtered to include only the strongest 31\% of predictions, the predictive accuracy rose to 79\%.}, author = {Holley, L. H. and Karplus, M.}, citeulike-article-id = {4258762}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/2911565}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=2911565}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {amino, bias, prediction, protein, secondary, structure, terminal}, month = {January}, number = {1}, pages = {152--156}, posted-at = {2009-04-02 16:21:12}, priority = {2}, title = {Protein secondary structure prediction with a neural network.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/2911565}, volume = {86}, year = {1989} }

TY - JOUR ID - citeulike:4258762 L3 - citeulike-article-id:4258762 N2 - A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum overall predictive accuracy of 63% for three states: helix, sheet, and coil. A numerical measure of helix and sheet tendency for each residue was obtained from the calculations. When predictions were filtered to include only the strongest 31% of predictions, the predictive accuracy rose to 79%. IS - 1 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 0027-8424 EP - 156 TI - Protein secondary structure prediction with a neural network. VL - 86 SP - 152 KW - amino KW - bias KW - prediction KW - protein KW - secondary KW - structure KW - terminal AU - Holley, LH AU - Karplus, M PY - 1989/01// UR - http://view.ncbi.nlm.nih.gov/pubmed/2911565 ER -