Applying Self-Organizing Map in Clustering Protein Phosphorylation Sequence Data

• Main Authors: Pei Ling Chung, 鍾佩陵
• Other Authors: C. H. Chen
• Format: Others
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/17423003007788194328

碩士 === 長庚大學 === 資訊管理學系 === 98 === The analysis of the surrounding sequences of protein phosphorylation sites is a very important topic in the protein and biology related research. However, the current data mining techniques applied on protein phosphorylation sites research mostly focus on building classification models to forecast the phosphorylation sites. This research applies cluster analysis in protein phosphorylation site sequences to assist biomedical researchers to filter through possible targets with reduced time and effort. Self-organizing map (SOM) is an often used cluster analysis method in biomedical data processing and with proven effectiveness. This thesis used the physical-chemical properties of amino acids and binary coding to encode protein phosphorylation site sequences; different topology of SOM is also used to explore the protein phosphorylation site surrounding sequences data which regulated by specific protein kinase. This research conducts cluster analyses on the PKA group kinase-related and CK2 group kinase-related protein phosphorylation site sequence data. The cluster analysis results of two data coding methods were evaluated, compared, and analyzed by five evaluation indicators and two color representation methods, and the results show that the cluster analysis on data coding using the physical-chemical properties of amino acid can better separate amino acid sequence of similar properties.