Matching 2D patterns of protein spots
Proceedings of the fourteenth annual symposium on Computational geometry
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Data mining: concepts and techniques
Data mining: concepts and techniques
Geometric algorithms for the analysis of 2D-electrophoresis gels
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Physical Database Design for Data Warehouses
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Metric Incremental Clustering of Nominal Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A spot-matching framework for improving matching accuracy in protein 2-DE gel image analysis
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
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Objective:: Two-dimensional electrophoresis (2DE) is a separation technique that can identify target proteins existing in a tissue. Its result is represented by a gel image that displays an individual protein in a tissue as a spot. However, because the technique suffers from low reproducibility, a user should manually annotate landmark spots on each gel image to analyze the spots of different images together. This operation is an error-prone and tedious job. For this reason, this paper proposes a method of extracting landmark spots automatically by using a data mining technique. Method and material:: A landmark profile which summarizes the characteristics of landmark spots in a set of training gel images of the same tissue is generated by extracting the common properties of the landmark spots. On the basis of the landmark profile, candidate landmark spots in a new gel image of the same tissue are identified, and final landmark spots are determined by the well-known A^* search algorithm. Result and conclusions:: The performance of the proposed method is analyzed through a series of experiments in order to identify its various characteristics.