Open AccessComputational analysis of crystallization trials
Author(s) -
Spraggon Glen,
Lesley Scott A.,
Kreusch Andreas,
Priestle John P.
Publication year - 2002
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
ISSN - 1399-0047
DOI - 10.1107/s0907444902016840
Subject(s) - categorization , computer science , artificial intelligence , set (abstract data type) , pattern recognition (psychology) , crystallization , enhanced data rates for gsm evolution , artificial neural network , data mining , engineering , chemical engineering , programming language
A system for the automatic categorization of the results of crystallization experiments generated by robotic screening is presented. Images from robotically generated crystallization screens are taken at preset time intervals and analyzed by the computer program Crystal Experiment Evaluation Program ( CEEP ). This program attempts to automatically categorize the individual crystal experiments into a number of simple classes ranging from clear drop to mountable crystal. The algorithm first selects features from the images via edge detection and texture analysis. Classification is achieved via a self‐organizing neural net generated from a set of hand‐classified images used as a training set. New images are then classified according to this neural net. It is demonstrated that incorporation of time‐series information may enhance the accuracy of classification. Preliminary results from the screening of the proteome of Thermotoga maritima are presented showing the utility of the system.