title: GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network.
abstract: With the advance of modern molecular biology it has become increasingly clear that few cellular processes are unaffected by protein phosphorylation. Therefore, computational identification of phosphorylation sites is very helpful to accelerate the functional understanding of huge available protein sequences obtained from genomic and proteomic studies. Using a genetic algorithm integrated neural network (GANN), a new bioinformatics method named GANNPhos has been developed to predict phosphorylation sites in proteins. Aided by a genetic algorithm to optimize the weight values within the network, GANNPhos has demonstrated a high accuracy of 81.1, 76.7 and 73.3% in predicting phosphorylated S, T and Y sites, respectively. When benchmarked against Back-Propagation neural network and Support Vector Machine algorithms, GANNPhos gives better performance, suggesting the GANN program can be used for other prediction tasks in the field of protein bioinformatics.
authors.affiliation: Bioinformatics Center, College of Biological Sciences, China Agricultural University, Beijing 100094, China.
authors.forename: Yu-Rong
authors.initials: YR
authors.lastname: Tang
authors.forename: Yong-Zi
authors.initials: YZ
authors.lastname: Chen
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Canchaya
authors.forename: Ziding
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019985:Benchmarking; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010766:Phosphorylation; D011237:Predictive Value of Tests; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry
chemical_list: D011506:Proteins