Informatics Research Institute, Mubarak City for Science and Technology, Borg ElArab, Alexandria, Egypt.
Adv Exp Med Biol. 2010;680:215-27. doi: 10.1007/978-1-4419-5913-3_25.
Two-dimensional polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at the development of tools for expanding the range of proteins accessible with two-dimensional gels. Proteomics was built around the two-dimensional gel. The idea that multiple proteins can be analyzed in parallel grew from two-dimensional gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time consuming, labor extensive and error prone. It is desired that the computer can analyze the proteins automatically by first detecting, then quantifying the protein spots in the 2D gel images. This paper focuses on the protein spot detection and segmentation of 2D gel electrophoresis images. We present a new technique for segmentation of 2D gel images using the Fuzzy C-Means (FCM) algorithm and matching spots using the notion of fuzzy relations. Through the experimental results, the new algorithm was found out to detect protein spots more accurately, then the current known algorithms.
蛋白质的二维聚丙烯酰胺凝胶电泳是一种强大且可重复的技术。它是蛋白质组学中应用最广泛的分离工具。目前该领域的研究重点是开发可扩展二维凝胶可及性范围的工具。蛋白质组学是围绕二维凝胶建立的。可以同时分析多种蛋白质的想法源于二维凝胶图谱。蛋白质组学研究人员需要通过检查凝胶来识别感兴趣的蛋白斑点。这既耗时又费力，而且容易出错。人们希望计算机能够通过首先检测，然后量化二维凝胶图像中的蛋白斑点来自动分析蛋白质。本文专注于二维凝胶电泳图像中的蛋白斑点检测和分割。我们提出了一种使用模糊 C 均值（FCM）算法分割二维凝胶图像的新技术，并使用模糊关系的概念来匹配斑点。通过实验结果，发现新算法比当前已知的算法更能准确地检测蛋白斑点。
