title: Neural networks predict protein structure and function.
abstract: Both supervised and unsupervised neural networks have been applied to the prediction of protein structure and function. Here, we focus on feedforward neural networks and describe how these learning machines can be applied to protein prediction. We discuss how to select an appropriate data set, how to choose and encode protein features into the neural network input, and how to assess the predictor's performance.
authors.affiliation: Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
authors.forename: Marco
authors.initials: M
authors.lastname: Punta
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D018360:Crystallography, X-Ray; D030541:Databases, Genetic; D030562:Databases, Protein; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D008962:Models, Theoretical; D009415:Nerve Net; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis / Q000737:chemistry*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins