an fpt approach for predicting protein localization from yeast genomic data把一种方法预测蛋白质从酵母基因组数据本地化.pdfVIP

An FPT Approach for Predicting Protein Localization from Yeast Genomic Data 1,2 1,3 1 4 Jin Wang *, Chunhe Li , Erkang Wang *, Xidi Wang 1 State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, China, 2 Departments of Chemistry, Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, New York, United States of America, 3 Graduate School of the Chinese Academy of Sciences, Beijing, China, 4 Citibank, Sao Paulo, Brazil Abstract Accurately predicting the localization of proteins is of paramount importance in the quest to determine their respective functions within the cellular compartment. Because of the continuous and rapid progress in the fields of genomics and proteomics, more data are available now than ever before. Coincidentally, data mining methods been developed and refined in order to handle this experimental windfall, thus allowing the scientific community to quantitatively address long- standing questions such as that of protein localization. Here, we develop a frequent pattern tree (FPT) approach to generate a minimum set of rules (mFPT) for predicting protein localization. We acquire a series of rules according to the features of yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regression under various statistical measures. Our results show that mFPT gave better performance than other approaches in predicting protein localization. Meanwhile, setting 0.65 as the minimum hit-rate, we obtained 138 proteins that mFPT predicted differently than the simple naive bayesian method (SNB). In our analysis of these 138 proteins, we present