报 告 人：宋九州（美国马里兰大学教授）
报告题目: Evaluation of Clustering Analysis and Term-tissue specific models for Temporal Gene Expression Data with PCA Test and SVM
报告内容摘要: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes. This study focused on using support vector machines (SVM) to predict GO biological processes from individual or multiple-tissue transcriptional profiles of aging in Drosophila melanogaster. Ten-fold cross validation was implemented to evaluate the prediction. One-tail Fisher’s exact test was conducted on each cross validation and multiple testing was addressed using BH FDR procedure. The results showed that, of the 148 pursued GO biological processes, fifteen terms each had at least one model with FDR-adjusted p-value (Adj.p) <0.05 and six had the values between 0.05 and 0.25. Furthermore, all these models had the prediction sensitivity (SN) over 30% and specificity (SP) over 80%. We proposed the concept of term-tissue specific models indicating the fact that the major part of the optimized prediction models was trained from individual tissue data. Furthermore, we observed that the memberships of the genes involved in all the three pursued children biological processes on mitochondrial electron transport could be predicted from the transcriptional profiles of aging (Adj.p < 0.01). This finding may be important in biology because the genes of mitochondria play a critical role in the longevity of C. elegans and D. melanogaster.
宋九州博士，美国马里兰大学农业与自然资源学院动物与禽类科学系教授，目前主要从事生物统计，统计基因组，生物信息学和基因网络等生物领域的科学研究。近五年发表高质量的论文十余篇。2005至现在：任American Society of Mathematical Biology 和International Society of Animal Genetics 委员，2007至现在：任American Biological Safety Association 委员。