• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.79-84
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• 2010

Matching spots between two sets of 2-dimensional electrophoresis can make it possible to find out the generation, extinction and change of proteins. Generally protein spots are separated by 2-dimensional electrophoresis. This process makes the position of the same protein spot a little different according to the status of the tissue or the experimental environment. Matching the spots shows that the relation of spots is non-uniform and non-linear transformation. However we can also find that the local relation preserves the topology. This study proposes a matching method motivated by the preservation of the topology. To compare the similarity of the topology, we compared the distance and the angle between neighbour spots. Experimental result shows that the proposed method is effective.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.323-328
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• 2006

Proteins in a cell appear as spots in a two dimensional gel image which is used in protein analysis. The spots from the same protein are in near position when comparing two gel images. Finding out the different proteins between a normal tissue and a cancer one is important information in drug development. Automatic matching of gel images is difficult because they are made from biological experimental processes. This matching problem is known to be NP-hard. Neural networks are usually used to solve such NP-hard problems. Hopfield neural network is selected since it is appropriate to solve the gel matching. An energy function with location and distance parameters is defined. The two spots which make the energy function minimum are matching spots and they came from the same protein. The energy function is designed to reflect the topology of spots by examining not only the given spot but also neighborhood spots.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.561-574
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• 2008

The spot detection phase of the 2-DE image analysis program segments a gel image into spot regions by an image segmentation algorithm and fits the spot regions to a spot shape model and quantifies the spot informations for the next phases. Currently the watershed algorithm is generally used as the segmentation algorithm and there are the Gaussian model and the diffusion model for the shape model. The diffusion model is closer to real spot shapes than the Gaussian model however spots have very various shapes and especially an asymmetric formation in x-coordinate and y-coordinate. The reason for asymmetric formation of spots is known that a protein could not be diffused completely because the 2-DE could not be processed under the ideal environment usually. Accordingly we propose an asymmetric diffusion model in this paper. The asymmetric diffusion model assumes that a protein spot is diffused from a disc at initial time of diffusing process, but is diffused asymmetrically for x-axis and y-axis respectively as time goes on. In experiments we processed spot matching for 19 gel images by using three models respectively and evaluated averages of SNR for comparing three models. As averages of SNR we got 14.22dB for the Gaussian model, 20.72dB for the diffusion model and 22.85dB for the asymmetric diffusion model. By experimental results we could confirm the asymmetric diffusion model is more efficient and more adequate for spot matching than the Gaussian model and the diffusion model.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.601-608
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• 2007

2 Dimensional Gel Electrophoresis(2DGE) is an essentialmethodology for analysis on the expression of various proteins. For example, information for the location, mass, expression, size and shape of the proteins obtained by 2DGE can be used for diagnosis, prognosis and biological progress by comparison of patients with the normal persons. Protein spot matching for this purpose is comparative analysis of protein expression pattern for the 2DGE images generated under different conditions. However, visual analysis of protein spots which are more than several hundreds included in a 2DGE image requires long time and heavy effort. Furthermore, geometrical distortion makes the spot matching for the same protein harder. In this paper, an iterative algorithm is introduced for more efficient spot matching. Proposed method is first performing global matching step, which reduces the geometrical difference between the landmarks and the spot to be matched. Thus, movement for a spot is defined by a weighted sum of the movement of the landmark spots. Weight for the summation is defined by the inverse of the distance from the spots to the landmarks. This movement is iteratively performed until the total sum of the difference between the corresponding landmarks is larger than a pre-selected value. Due to local distortion generally occurred in 2DGE images, there are many regions in whichmany spot pairs are miss-matched. In the second stage, the same spot matching algorithm is applied to such local regions with the additional landmarks for those regions. In other words, the same method is applied with the expanded landmark set to which additional landmarks are added. Our proposed algorithm for spot matching empirically proved reliable analysis of protein separation image by producing higher accuracy.