統計学輪講（第２回）

日時      2006年 4月 25日(火)    15時〜16時40分
場所      経済学部新棟３階第２教室 [※ 今年度は教室が変更になっております]
講演者    宮田 敏(財団法人癌研究会ゲノムセンター)
演題      Medical informatics for OMICS data
概要：
  Recently many powerful technologies for detection of genome related
data have been developed and utilized to explore important biomarkers
and genes related to treatment response. We discuss about high
throughput genome-related data, including single nucleotide
polymorphism(SNP), DNA microarray data for gene expression, and
protein expression profile data. We also argue about bioinformatics
methodologies suitable for analysis of genome-related data. 
  The SNP data characterize genetic constitution in patients and are,  
for example, used to predict adverse effect of medicine. We describe 
the association study of SNP data and the haplotype based analysis
thought to be more efficient than the individual SNP based approach. 
The gene and/or protein expression profile data are thought to reflect
genetic individuality and are used for personalized diagnosis of
patients. We introduce some useful methods, including AdaBoost, to
analyze these data. AdaBoost is robust outliers, capable of handling
missing values and appropriate for analysis of gene and/or protein
expression data. 
  To detect expression signals from protein profile data, it is
necessary to denoize row data which is inhomogeneously smooth. To
fit a trend line to protein profile data, we introduce adaptive
free-knot spline and adaptive model selection criterion(Miyata, S. 
and Shen, X. (2005)).

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Tokyo University