Outlier analyses of the Protein Data Bank archive using a probability-density-ranking approach.

ABSTRACT: Outlier analyses are central to scientific data assessments. Conventional outlier identification methods do not work effectively for Protein Data Bank (PDB) data, which are characterized by heavy skewness and the presence of bounds and/or long tails. We have developed a data-driven nonparametric method to identify outliers in PDB data based on kernel probability density estimation. Unlike conventional outlier analyses based on location and scale, Probability Density Ranking can be used for robust assessments of distance from other observations. Analyzing PDB data from the vantage points of probability and frequency enables proper outlier identification, which is important for quality control during deposition-validation-biocuration of new three-dimensional structure data. Ranking of Probab

SUBMITTER: Shao C 

PROVIDER: S-EPMC6289109 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature