01997nas a2200229   4500008004100000245005400041210005400095260003700149300001100186490000600197520136000203653000701563653000801570653001501578653001901593100001901612700002401631700001901655700002201674700002001696856005101716       2008                        eng d00aAutomated 3D Phenotype Analysis Using Data Mining0 aAutomated 3D Phenotype Analysis Using Data Mining  bPublic Library of SciencecMarch  ae1742+0 v33 aThe ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases.10a3d10agis10amorphology10areconstruction1 aPlyusnin, Ilya1 aEvans, Alistair, R.1 aKarme, Aleksis1 aGionis, Aristides1 aJernvall, Jukka  uhttp://dx.doi.org/10.1371/journal.pone.0001742