• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.551-555
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• 2010

In protein 2-DE image analysis, the accuracy of spot-matching operation which identifies the spot of the same protein in each 2-DE gel image is intensively influenced by the errors caused by the various experimental conditions. This paper proposes an efficient method to find more accurate spot-matching patterns based on multiple reference gel images in spot-matching pattern analysis in protein 2-DE image analysis. Additionally, in order to improve the reduce the execution time which is increased exponentially along with the increasing number of gel images, a "partition then extension" framework is used to find spot-matching pattern of long length and of higher accuracy. In the experiments on real 2-DE images of human liver tissue are used to confirm the accuracy and the efficiency of the proposed algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.3
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• pp.105-111
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• 2011

Landmark Spots in 2D gel electrophoresis are used in many methods of 2DEG spot matching. Landmark Spots are obtained manually and it is a bottle neck in the entire protein analysis process. Automated landmark spots detection is a very crucial topic in processing a massive amount of 2DGE data. In this paper, Automated landmark spot detection is proposed using point pattern matching and graph theory. Neighbor spots are defined by a graph theory to use and only a centered spot and its neighbor spots are considered for spot matching. Normalized Hausdorff distance is introduced as a criterion for measuring degree of similarity. In the conclusion, the method proposed in this paper can get about 50% of the total spot pairs and the accuracy rate is almost 100%, which the requirements of landmark spots are fully satisfied.

• 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.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.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.35-47
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• 2002

2D-PAGE/MALDI-TOF는 프로-테옴 연구의 중요한 실험 기법중의 하나이다. 이는 단백질의 발현 분석을 위한 방법으로, 2D-PAGE 결과로 얻어진 영상 데이터의 분석에 대한 정확도가 단백질 발현에 대한 분석 결과의 질을 결정하는 중요한 요인으로 작용한다. 2D Electrophoresis에 의한 Gel Protein Database는 현재 많은 연구자들에 의해 생산되고 있으며, 대단히 많은 데이터들이 인터넷을 통하여 접근이 가능하다. 이러한 대량 정보의 Database 활용이 가능한 상황은 2D-PAGE에 의해 생산된 Gel Image의 상호 비교에 대한 요구를 도출하였다. 본 발표에서는 영상처리 및 형태인식 기술과 2D-PAGE 연구의 결합을 주제로 하여, 2D-PAGE Gel 영상 처리 및 비교에 관련되는 전처리 (preprocessing), spot detection, feature extraction, spot matching 및 image comparison 기술을 소개한다.

• KSBB Journal
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• v.25 no.5
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• pp.478-482
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• 2010

Inhibitory melanogenesis by Bambusae caulis in Teaniam (Phyllostaachys nigra var. henonis Stapf) was studied. Tyrosinase inhibition activities were evaluated with six different extracts. Among them the extract with methanol showed the highest tyrosinase activity inhibition. MTT assay with B16 melanoma showed that the extract was not toxic up to the concentration of 50 ppm. The melanogenesis was clearly inhibited by the extract when it was examined by the melanin content assay in the cell. When the extract was dosed as 10 ppm, the melanogenesis was reduced to 68% in culture medium and 74% in the cell. By the proteome analysis with 2-D electrophoresis, 171 protein spots were found in the control gel and 282 spots were detected in the sample gel. Among 120 spot proteins matched, 12 spots were identified as proteins involved in the melanogenesis mechanism.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.455-458
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• 2008

단백질체학에서 2-DE는 조직내의 단백질을 규명하는 단백질 분리 기술로서 2-DE에 의하여 생성된 단백질 이미지에서 스팟 매칭을 진행하여 상이한 단백질 젤 내에 존재하는 동일한 단백질 클래스를 찾을 수 있다. 그러나 단백질 2-DE 이미지는 실험 환경의 변화에 민감하여 이미지의 위치적인 변형이나 먼지, 공기방울 등으로 인해 많은 에러 정보를 포함할 수 있다. 이러한 에러는 스팟 매칭에 치명적인 영향을 주어 낮은 정확도를 가지게 된다. 본 논문에서는 단백질 2-DE 이미지 분석을 위한 스팟 매칭에서의 정확도를 향상시키기 위하여 기준점 학습과 기준점 추출의 두 단계로 이루어진 자동화된 기준점 추출 방법을 사용하여 스팟 매칭의 정확도를 향상시킬 수 있는 최적의 기준점을 선정하는 방법을 제안하며 선정된 기준점을 기반으로 다수의 기준 이미지를 선택하여 스팟 매칭을 반복적으로 진행함으로써 확률 기반의 정확한 스팟 매칭 결과를 도출하고자 한다. 특히 데이터 마이닝 기법에서 사용되는 최소지지도 값을 적용함으로써 지지도가 높은 스팟 매칭 결과를 빈발한 스팟 매칭으로 판정한다. 제안한 스팟 매칭 정확도 향상 기법의 정확도를 평가하기 위하여 실제 단백질 2-DE 젤 이미지 데이터를 사용하여 입력 기준점의 개수와 최소 지지도의 증가에 따른 정확도의 변화를 분석하였다.