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生物统计及生物信息学前沿=Frontiers of Biostatistics and Bioinformatics:英文(“十一五”国家重点图书)

生物统计及生物信息学前沿
生物统计及生物信息学前沿=Frontiers of Biostatistics and Bioinformatics:英文(“十一五”国家重点图书)
作者:马双鸽 王跃东(主编)

图书详细信息:
ISBN:978-7-312-02228-9
定价:58.00元
版本:1
装帧:软精装
出版年月:200902
丛书名称:当代科学技术基础理论与前沿问题研究丛书:中国科学技术大学校友文库

图书简介:

  作为现代科学的一个重要分支,生物统计和生物信息学运用统计、数学和计算机科学的方法研究生物及医学问题。在本书中,我们收录了15篇文章介绍生物统计和生物信息学中统计方法的最新进展。由于篇幅有限,我们着重介绍了高维数据分析方法及其应用。和统计学的其他分支相比,高维数据分析方法及其应用还处在相对初级阶段,我们希望本书的出版能够推动此方向的研究。

前言:

Preface
  
  It would not be a stretch to say that modern statistics is enjoying a youthful bloom in greater China. The Chinese have long been renowned for fundamental contributions to the foundations of scince and mathematical thinking. We expect no less from future generations of Chinese statisticians.
  At the dawn of the 20th century, statistics had but a small role on the stage of scientific practice; by the close of the century, the use of statistics
was ubiquitous in virtually every area of scientific and practical interest, with statisticians having developed many procedures to address substantive prob-
lems arising in these various areas. In the last 25 years especially, proceeding in hand with the exponential growth in computing power, new technologies
have emerged in various disciplines, leading to data sets that are orders of magnitude larger than those for which the earlier procedures were developed.
This prompts questions which these earlier procedures are not well suited to address. Substantial statistical innovations are necessary to take on the task,
and while statisticians are responding well to the challenge, much work remains to be done.
  This volume, occasioned by the University of Science and Technology (USTC) 50th anniversary celebration, features 15 statistical papers by renowned statisticians and USTC alumni covering 4 topics in high dimensional data analysis and bioinformatics at the frontiers of modern statistical science: (i)statistical genetics, (ii) the analysis of microarray data, (iii) computational biology and statistical learning, and (iv) statistical methods for analyzing
high dimensional data.
  The three statistical genetics papers cover several important topics in the area. Cui, Zhang, Yang and Li address the problems of bias and power in linkage analysis with mixed affected sibling pair data. They propose three test procedures to address these issues, and show that all three perform satisfactorily. Chen reviews sequential importance sampling algorithms developed in population genetics, as well as a more recently proposed technique developed by Chen that incorporates resampling. Lin proposes a hierarchical Bayesian approach for detecting QTL using model selection techniques. The approach works well, provided the number of markers is not very large.
  Four papers address the analysis of microarray data. By incorporating longitudinal information on gene expression, Hong develops a functional
hierarchical empirical Bayes approach for detecting TR and TDE genes from MTC gene expression experiments. Using a smoothness assumption on the gene expression trajectories, the gene expression profiles are modeled and approximated by well known basis function expansions. The paper by Lai ad-
dresses, in the context of FDR, the problem of estimating the fraction of null hypotheses that are true when a large number of tests are performed; using a
nonparametric method, an upper bound on the fraction is obtained. Based on earlier work on normalization methods for microarray data, and information
on non-replicating genes, Peng proposes new methods that lead to improved estimation of the intensity functions. In connection with the need for reliable
variance estimation for gene expression microarray data, Tong and Wang review several statistical methods for estimating variances in the "large p, small
n" context.
  Four papers are addressed to the subject of computational biology and statistical learning. Feng, Xu, Zhang, Li, Xie and Wang study the problem of
predicting protein subcellular locations using a machine learning type of approach. Through experiments and comparisons, the authors conclude that us-
ing the PSSM generated from PSI-BLAST as input and SVM as classifier leads to better predictive performance. A new learning method called LOCSVMPSI
is proposed and recommended based on its even better performance. Chao and Jiang give a general review of methods for extracting information at the biomolecular sequence level, with emphasis on biological sequence alignment.  Lin, Simmons, Beecher, rlyuoung and Young apply various classification methods,including RP, SVM, and RF, to identify an important set of metabolites for disease classification. Wang and Xi survey recent developments in the statistical modeling of chromatin Sequences. They argue that chromatin sequences trained by a previously proposed model called DHMM may have larger power in
predicting the correct nucleosome positioning.
  The final set of papers is concerned with high dimensional data analysis. Chen gives a comprehensive review of recent developments in finite mixture
modeling. Guo and Dai propose an iterative procedure to fit a smoothing spline ANOVA model with heterogeneous variances. The method is then ap- plied to a
data set with a sample of epileptics. Zeng and Yu address the issue of bias arising in kernel estimation in longitudinal studies. They propose a biascorrected procedure and derive its large sample properties. Zou derives a computable bound in evaluating the quality of the Gibbs sampler, in the context ofestimating the posterior mode of the Lasso distribution.
  The bound has direct implications for deriving the Lasso estimator. The papers in this collection illustrate both the types of challenges statisticians
face and will continue to face and as well as the opportunities such challenges open up for new statistical work. On one hand, statisticians need to look
inward and develop better statistical methodologies and procedures, and on the other, to look outward and work with researchers in other disciplines to
meet the new challenges they bring to the table. There is every reason to believe that statistics in the 2lst century will continue to be as exciting an area as it was in the 20th century, and the need to train and develop a talented generation of younger statisticians will be great. We hope that USTC and its alumni will continue its work along these line and we trust that the next 50 years for USTC will be even more fruitful and exciting than the 50 past years
that we celebrate today.
 

  Shaw-Hwa Lo
Columbia Universitv

目录:

Contents

Preface to the USTC Alumni's Series

From the Editors

Preface

Section I   Statistical Genetics

Section II   Statistical Analysis of Microarray Data

Section III   Computational Biology

Section IV   General Methodology

Brief Introduction of Authors



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