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 Probability Density also permits use of Most Probable Range as a
robust measure of data dispersion that is more compact than Interquartile Range.
The Probability-Density-Ranking approach can be employed to analyze outliers and
data-spread on any large data set with continuous distribution.