生物统计学和生物信息学最新进展

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

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

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

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。

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生物信息


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用户评论 (总计27条)

 
 

  •     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。
  •     比较全面而基础的书,内容详实
  •     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
  •     我喜欢,送货速度快。
  •     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
 

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