Validating accuracy of web server statistics
To understand and control those functions, we need accurate knowledge about the models that represent those structures, including their many strong points and their occasional weaknesses.
End-users of macromolecular models include clinicians, teachers and students, as well as the structural biologists themselves, journal editors and referees, experimentalists studying the macromolecules by other techniques, and theoreticians and bioinformaticians studying more general properties of biological molecules.
Herein, a new web server, Kinase Phos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites.
The coupling pattern [ between the positive set of phosphorylation sites and the background set of whole protein sequences from Swiss-Prot are computed to determine the number of coupling patterns for training SVM models.
Referred to our previous work, Kinase Phos 1.0, incorporated profile hidden Markov model (HMM) with flanking residues of the kinase-specific phosphorylation sites.The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics.This flexibility of protein structures is associated with various biological processes. Tilt-pair validation analysis (Rosenthal and Henderson, 2003) can be used to assess the accuracy of initial angle assignment in single-particle processing.
To perform this analysis you need to collect two corresponding sets of particle images - one untilted and the other tilted, then upload the stacks of images along with a 3D reconstruction based on the untilted images.Small-molecule diffraction data extends to much higher resolution than feasible for macromolecules, and has a very clean mathematical relationship between the data and the atomic model.The residual, or R-factor, measures the agreement between the experimental data and the values back-calculated from the atomic model.Basically sensitivity to noise (when classification produces random result) is a common definition of overfitting (see wikipedia): In statistics and machine learning, one of the most common tasks is to fit a "model" to a set of training data, so as to be able to make reliable predictions on general untrained data.