A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications.
We classified clusters into three types: having homologs (BLASTP matches), having remote homologs (PDB-BLAST or FFAS matches), or having no recognizable homologs in the known protein space.
We classified clusters of the Tc1-mariner-IS630 Tc1-mariner-IS630 Tc1-mariner-IS630groups.
We then classified clusters as those randomised to feedback-on in the first period or those first randomised to feedback-off.
It classifies clusters of generators to be equivalenced by their electrical distances, and identifies second order reduced models for them.
The distribution of mass in clusters of galaxies is being traced through detailed x-ray images, and the data are being used to classify clusters and trace their formation and evolution.
OrthoMCL clusters were postprocessed to classify clusters by their species content and analyzed with reference to the robust molecular phylogeny of the Nematoda (25).
Maximal weighted distances within a cluster are used to classify clusters into good and bad; (3) subsequent cluster division relies on edge structure, which probabilistic linkage does not do.
We still chose to classify clusters in these silent gene regions as transcript clusters, as these signatures were most likely a result of stalled transcription [ 37] and not related to long-range interactions.
Given that the raw pBLS value, which is not adjusted for multiple testing, is a continuous estimate of the cluster constraint strength, classifying clusters into significant and nonsignificant unbalanced categories will yield a further loss of statistical power (Pearson 1913).
Several researchers have classified and clustered web spam pages.
