所属分类：生物科学

出版时间：2008-12 出版时间：高等教育出版社 作者：范剑青，林希虹，刘军 主编 页数：269

Tag标签：生物信息

**前言**

The first eight years of the twenty-first century has witted the explosion of datacollection, with relatively low costs. Data with curves, images and movies are fre-quently collected in molecular biology, health science, engineering, geology, clima-tology, economics, finance, and humanities. For example, in biomedical research,MRI, fMRI, microarray, and proteomics data are frequently collected for eachsubject, involving hundreds of subjects; in molecular biology, massive sequencingdata are becoming rapidly available; in natural resource discovery and agricul-ture, thousands of high-resolution images are collected; in business and finance,millions of transactions are recorded every day. Frontiers of science, engineering,and humanities differ in the problems of their concerns, but nevertheless share acommon theme: massive or complex data have been collected and new knowledgeneeds to be discovered. Massive data collection and new scientific research havestrong impact on statistical thinking, methodological development, and theoreti-cal studies. They have also challenged traditional statistical theory, methods, andcomputation. Many new insights and phenomena need to be discovered and newstatistical tools need to be developed.With this background, the Center for Statistical Research at the ChineseAcademy of Science initiated the conference series "International Conference onthe Frontiers of Statistics" in 2005. The aim is to provide a focal venue for re-searchers to gather, interact, and present their new research findings, to discussand outline emerging problems in their fields, to lay the groundwork for future col-laborations, and to engage more statistical scientists in China to conduct researchin the frontiers of statistics. After the general conference in 2005, the 2006 Inter-national Conference on the Frontiers of Statistics, held in Changchun, focused onthe topic "Biostatistics and Bioinformatics". The conference attracted many topresearchers in the area and was a great success. However, there are still a lot ofChinese scholars, particularly young researchers and graduate students, who werenot able to attend the conference. This hampers one of the purposes of the con-ference series. However, an Mternative idea was born: inviting active researchersto provide a bird-eye view on the new developments in the frontiers of statistics,on the theme topics of the conference series. This will broaden significantly thebenefits of statistical research, both in China and worldwide. The edited books inthis series aim at promoting statistical research that has high societal impacts andprovide not only a concise overview on the recent developments in the frontiers ofstatistics, but also useful references to the literature at large, leading readers trulyto the frontiers of statistics.

**内容概要**

《生物统计学和生物信息学最新进展》主要内容：presents an overview of recent developments in biostatistics and bioinformatics. Written by active researchers in these emerging areas, it is intended to give graduate students and new researchers an idea of where the frontiers of biostatistics and bioinformatics are as well as a forum to learn common techniques in use, so that they can advance the fields via developing new techniques and new results. Extensive references are provided so that researchers can follow the threads to learn more comprehensively what the literature is and to conduct their own research. In particulars, the book covers three important and rapidly advancing topics in biostatistics: analysis of survival and longitudinal data, statistical methods for epidemiology.

**书籍目录**

PrefacePart

Ⅰ

Analysis

of

Survival

and

Longitudinal

DataChapter

1

Non-

and

Semi-

Parametric

Modeling

in

Survival

Analysis

1

Introduction

2

Cox's

type

of

models

3

Multivariate

Cox's

type

of

models

4

Model

selection

on

Cox's

models

5

Validating

Cox's

type

of

models

6

Transformation

models

7

Concluding

remarks

ReferencesChapter

2

Additive-Accelerated

Rate

Model

for

Recurrent

Event

1

Introduction

2

Inference

procedure

and

asymptotic

properties

3

Assessing

additive

and

accelerated

covariates

4

Simulation

studies

5

Application

6

Remarks

Acknowledgements

Appendix

ReferencesChapter

3

An

Overview

on

Quadratic

Inference

Function

Approaches

for

Longitudinal

Data

1

Introduction

2

The

quadratic

inference

function

approach

3

Penalized

quadratic

inference

function

4

Some

applications

of

QIF

5

Further

research

and

concluding

remarks

Acknowledgements

ReferencesChapter

4

Modeling

and

Analysis

of

Spatially

Correlated

Data

1

Introduction

2

Basic

concepts

of

spatial

process

3

Spatial

models

for

non-normal/discrete

data

4

Spatial

models

for

censored

outcome

data

5

Concluding

remarks

ReferencesPart

Ⅱ

Statistical

Methods

for

EpidemiologyChapter

5

Study

Designs

for

Biomarker-Based

Treatment

Selection

1

Introduction

2

Definition

of

study

designs

3

Test

of

hypotheses

and

sample

size

calculation

4

Sample

size

calculation

5

Numerical

comparisons

of

efficiency

6

Conclusions

Acknowledgements

Appendix

ReferencesChapter

6

Statistical

Methods

for

Analyzing

Two-Phase

Studies

1

Introduction

2

Two-phase

case-control

or

cross-sectional

studies

3

Two-phase

designs

in

cohort

studies

4

Conclusions

ReferencesPart

Ⅲ

BioinformaticsChapter

7

Protein

Interaction

Predictions

from

Diverse

Sources

1

Introduction

2

Data

sources

useful

for

protein

interaction

predictions

3

Domain-based

methods

4

Classification

methods

5

Complex

detection

methods

6

Conclusions

Acknowledgements

ReferencesChapter

8

Regulatory

Motif

Discovery"

From

Decoding

to

Meta-Analysis

1

Introduction

2

A

Bayesian

approach

to

motif

discovery

3

Discovery

of

regulatory

modules

4

Motif

discovery

in

multiple

species

5

Motif

learning

on

ChiP-chip

data

6

Using

nucleosome

positioning

information

in

motif

discovery

7

Conclusion

ReferencesChapter

9

Analysis

of

Cancer

Genome

Alterations

Using

Singk

Nucleotide

Polymorphism

(SNP)

Microarrays

1

Background

2

Loss

of

heterozygosity

analysis

using

SNP

arrays

3

Copy

number

analysis

using

SNP

arrays

4

High-level

analysis

using

LOH

and

copy

number

data

5

Software

for

cancer

alteration

analysis

using

SNP

arrays

6

Prospects

Acknowledgements

ReferencesChapter

10

Analysis

of

ChiP-chip

Data

on

Genome

Tiling

Microarrays

1

Background

molecular

biology

2

A

ChiP-chip

experiment

3

Data

description

and

analysis

4

Follow-up

analysis

5

Conclusion

ReferencesSubject

IndexAuthor

Index

**章节摘录**

插图：We

assume

that

patients

can

be

divided

into

twogroups

based

on

an

assay

of

a

biomarker.

This

biomarker

could

be

a

compositeof

hundreds

of

molecular

and

genetic

factors,

for

example,

but

in

this

case

wesuppose

that

a

cutoff

value

has

been

determined

that

dichotomizes

these

values.In

our

example

the

biomarker

is

the

expression

of

guanylyl

cyclase

C

（GCC）

in

thelymph

nodes

of

patients.

We

assume

that

we

have

an

estimate

of

the

sensitivityand

specificity

of

the

biomarker

assay.

The

variable

of

patient

response

is

takento

be

continuous-valued;

it

could

represent

a

measure

of

toxicity

to

the

patient,quality

of

life,

uncensored

survival

time,

or

a

composite

of

several

measures.

Inour

example

we

take

the

endpoint

to

be

three-year

disease

recurrence.We

consider

five

study

designs,

each

addressing

its

own

set

of

scientific

ques-tions,

to

study

how

patients

in

each

marker

group

fare

with

each

treatment.

Al-though

consideration

of

which

scientific

questions

are

to

be

addressed

by

the

studyshould

supersede

consideration

of

necessary

sample

size,

we

give

efficiency

com-parisons

here

for

those

cases

in

which

more

than

one

design

would

be

appropriate.One

potential

goal

is

to

investigate

how

treatment

assignment

and

patient

markerstatus

affect

outcome,

both

separately

and

interactively.

The

marker

under

con-sideration

is

supposedly

predictive:

it

modifies

the

treatment

effect.

We

may

wantto

verify

its

predictive

value

and

to

assess

its

prognostic

value,

that

is,

how

wellit

divides

patients

receiving

the

same

treatment

into

different

risk

groups.

Eachstudy

design

addresses

different

aspects

of

these

overarching

goals.This

paper

is

organized

as

follows:1.

Definition

of

study

designs2.

Test

of

hypotheses3.

Sample

size

calculation4.

Numerical

comparison

of

efficiency5.

Conclusions2

Definition

of

study

designsThe

individual

study

designs

are

as

follows.2.1

Traditional

designTo

assess

the

safety

and

efficacy

of

the

novel

treatment,

the

standard

design

（Fig.

1）is

to

register

patients,

then

randomize

thorn

with

ratio

~~

to

receive

treatment

Aor

B.

We

compare

the

response

variable

across

the

two

arms

of

the

trial

withoutregard

for

the

marker

status

of

the

patients.In

our

example,

we

would

utilize

this

design

if

we

wanted

only

to

compare

therecurrence

rates

of

colorectal

cancer

in

the

two

treatment

groups

independent

ofeach

patient's

biomarker

status.

**编辑推荐**

《生物统计学和生物信息学最新进展》是由高等教育出版社出版的。

**图书封面**

**图书标签Tags**

生物信息

- 全英的，很喜欢这本书，比我想的还要好，内容需要好好学才行的
- 09年的书 买晚了，可惜。。。
- 适合从事这方面研究的专业人士。
- 高位数据应用
- 本书是一个论文集成，由最前沿科学家撰写。
- 从研究的角度来讲,内容都比较陈旧,都是作者们在若干年前发表在期刊上的论文.
- 这一行今年发展太快了。生物信息学部分的东西好多都过时了。
- 唯一的缺点应用写的比较浅,对我很有用。
- 真正拜读中,适合初接触系统生物学的
- 一本很有用的书,很全面很细致。
- 非常好的一本书,还是挺专业的。
- 值得购买的好书,有简单的数学原理描述。
- 比较难懂,当当就是可靠
- 内容丰富,讲的太粗了
- 需要有一定的数学基础的书,比较体贴的
- 可惜我需要的网络方面的内容很少。对序列等内容介绍较多,生物信息专业的一本经典书籍
- 很实用,应该不错
- 很好的一本书,感觉挺深奥的。专业性挺强的。搞科研时可以参考。
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- 书很厚,这本书涉及到的生物学应用软件使用方法很多
- 值得再三研读。,买来看看
- 适合入门者学习。,学了这么久
- 结合了国内外的最新成果,很喜欢的商品
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- 解答了很多疑问,很方便的帮助我们用软件解决问题
- 这本书写的很详细,就是运输过程有损坏
- 本科生看这有点难,对生物信息学入门介绍比较全面