蛋白质网络与途径分析（导读版） 下载 pdf 百度网盘 epub 免费 2025 电子书 mobi 在线

从组学的生物时代开始，科学家一直追求的是降低基因组规模试验的复杂性，以便于了解其蕴含的基本生物学原理。在《蛋白质网络与途径分析》这本《蛋白质网络与途径分析（导读版）》，专家从业人员汇编了函数数据分析的方法，经常被称为系统生物学，它被应用于药物研发、医学和基础医学领域的研究中。《蛋白质网络与途径分析（导读版）》分为三部分：1）对蛋白质、化合物和基因之间相互作用的阐述；2）介绍了网络、相互作用组和本体论研究中常用的分析工具；3）函数分析的应用范围。作为非常著名的《分子生物学方法》系列丛书之一，《蛋白质网络与途径分析（导读版）》提供了详细的说明，并且为动手实践提供了建议。

权威和前沿的《蛋白质网络与途径分析》既阐明了生物实验室试验方法，又介绍了相关计算工具，涵盖了这个令人着迷的新兴领域中大多数的问题。

目录

前言 v

撰稿人 ix

**部分：相互作用

1. 用Linguamatics公司研发的I2E软件从发表的文献中挖掘蛋白质相互作用 3

Judith B and y，David Milward，and Sarah McQuay

2. 基因组规模实验中转录因子与DNA结合的相对亲和力、特异性和敏感度 15

Vladimir A.Kuznetsov

3. 抑制因子-靶标数据的管理：步骤和在途径分析上的作用 51

Sreenivas Devidas

4. 用功能蛋白质芯片描绘蛋白质相互作用网络 63

Dawn R.Mattoon and Barry Schweitzer

5. 蛋白质相互作用的手工注释 75

Svetlana Bureeva，Svetlana Zvereva，Valentin Romanov， and Tatiana Serebryiskaya

第二部分：分析

6. 基因集富集分析 99

Charles A.Tilford and NathanO.Siemers

7. PANTHER途径：一个整合了数据分析工具且基于本体的途径数据库 123

HuaiyuMi and PaulThomas

8. 采用网络分析优化排序影响途径的基因 141

AaronN.Chang

9. 从多样的功能基因组数据中发掘生物学网络 157

Chad L.Myers，Camelia Chiriac， and Olga G.Troyanskaya

10. 在基于知识的集成平台上对组学数据及小分子化合物的函数分析 177

Yuri Nikolsky，Eugene Kirillov，Roman Zuev，Eugene Rakhmatulin， and Tatiana Nikolskaya

11. 动力学模型作为一种整合多层次动态实验数据的工具 197

Ekaterina Mogilevskaya，Natalia Bagrova，Tatiana Plyusnina，Nail Gizzatkulov，Eugeniy Metelkin，Ekaterina Goryacheva，Sergey Smirnov，Yuriy Kosinsky，Aleks and er Dorodnov，Kirill Peskov，Tatiana Karelina，Igor Goryanin， and OlegDemin

12. Cytoscape：用于网络建模的一个基于社区的框架 219

Sarah Killcoyne，Gregory W.Carter，Jennifer Smith，and John Boyle

13. 用语义数据集成和知识管理表示生物网络相关性 241

Sascha Losko and Klaus Heumann

14. 复杂的、多数据类型及多工具分析的解决方案：运用工作流程与流水线方法的 原则及应用 259

Robin E.J.Munro and YikeGuo

第三部分：应用

15. 高通量siRNA筛选结合化合物筛选作为一种干扰生物系统以及识别目标途径的方法 275

Jeff Kiefer，Hongwei H.Yin，QiangQ.Que， and Spyro Mousses

16. 用高密度等位基因关联数据进行途径和网络的分析 289

Ali Torkamani and Nicholas J.Schork

17. miRNAs：从生物起源到网络 303

Giuseppe Russo and Antonio Giordano

18. MetaMiner(CF)：疾病导向的生物信息学分析环境 353

Jerry M.Wright，Yuri Nikolsky，Tatiana Serebryiskaya， and Diana R.Wetmore

19. 转化研究与生物医学信息学 369

Michael Liebman

20. ArrayTrack：一个美国食品及药物管理局(FDA)和公共基因组工具 379

Hong Fang，StephenC.Harris，Zhenjiang Su，Minjun Chen，Feng Qian，Leming Shi，RogerPerkins， and Weida Tong

索引 399

(高友鹤 尹剑锐 译)

Contents

Preface v

Contributors ix

SECTION I：INTERACTIONS

1. Mining Protein–Protein Interactions from Published Literature Using Linguamatics I2E 3

Judith B and y，David Milward， and Sarah McQuay

2. Relative Avidity，Specificity， and Sensitivity of Transcription Factor–DNA Binding in Genome-Scale Experiments 15

Vladimir A. Kuznetsov

3. Curation of Inhibitor-Target Data：Process and Impact on Pathway Analysis 51

Sreenivas Devidas

4. Profiling Protein Interaction Networks with Functional Protein Microarrays 63

Dawn R. Mattoon and Barry Schweitzer

5. Manual Annotation of Protein Interactions 75

Svetlana Bureeva，Svetlana Zvereva，Valentin Romanov， and Tatiana Serebryiskaya

SECTION II：ANALYSIS

6. Gene Set Enrichment Analysis 99

Charles A. Tilford and Nathan O. Siemers

7. PANTHER Pathway：An Ontology-Based Pathway Database Coupled with Data Analysis Tools 123

Huaiyu Mi and Paul Thomas

8. Prioritizing Genes for Pathway Impact Using Network Analysis 141

Aaron N. Chang

9. Discovering Biological Networks from Diverse Functional Genomic Data 157

Chad L. Myers，Camelia Chiriac， and Olga G. Troyanskaya

10. Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated “Knowledge-Based” Platform 177

Yuri Nikolsky，Eugene Kirillov，Roman Zuev，Eugene Rakhmatulin， and Tatiana Nikolskaya

11. Kinetic Modeling as a Tool to Integrate Multilevel Dynamic Experimental Data 197

Ekaterina Mogilevskaya，Natalia Bagrova，Tatiana Plyusnina，Nail Gizzatkulov，Eugeniy Metelkin，Ekaterina Goryacheva，Sergey Smirnov，Yuriy Kosinsky，Aleks and er Dorodnov，Kirill Peskov，Tatiana Karelina，Igor Goryanin， and Oleg Demin

12. Cytoscape：A Community-Based Framework for Network Modeling 219

Sarah Killcoyne，Gregory W. Carter，Jennifer Smith， and John Boyle

13. Semantic Data Integration and Knowledge Management to Represent Biological Network Associations 241

Sascha Losko and Klaus Heumann

14. Solutions for Complex，Multi Data Type and Multi Tool Analysis：Principles and Applications of Using Workflow and Pipelining Methods 259

Robin E. J. Munro and Yike Guo

SECTION III：APPLICATIONS

15. High-Throughput siRNA Screening as a Method of Perturbation of Biological Systems and Identification of Targeted Pathways Coupled with Compound Screening 275

Jeff Kiefer，Hongwei H. Yin，Qiang Q. Que， and Spyro Mousses

16. Pathway and Network Analysis with High-Density Allelic Association Data 289

Ali Torkamani and Nicholas J. Schork

17. miRNAs：From Biogenesis to Networks 303

Giuseppe Russo and Antonio Giordano

18. MetaMiner (CF)：A Disease-Oriented Bioinformatics Analysis Environment 353

Jerry M. Wright，Yuri Nikolsky，Tatiana Serebryiskaya， and Diana R. Wetmore

19. Translational Research and Biomedical Informatics 369

Michael Liebman

20. ArrayTrack：An FDA and Public Genomic Tool 379

Hong Fang，Stephen C.Harris，Zhenjiang Su，Minjun Chen，Feng Qian，Leming Shi，Roger Perkins， and Weida Tong

Index 399

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SECTION Ⅰ INTERACTIONS

　　Chapter 1 Mining Protein–Protein Interactions from Published Literature Using Linguamatics I2E

　　Judith Bandy， David Milward， and Sarah McQuay

　　Abstract

　　Natural language processing (NLP) technology can be used to rapidly extract protein–protein interactions from large collections of published literature. In this chapter we will work through a case study using MEDLINE1 biomedical abstracts (1) to find how a specific set of 50 genes interact with each other. We will show what steps are required to achieve this using the I2E software from Linguamatics.

　　To extract protein networks from the literature， there are two typical strategies. The first is to find pairs of proteins which are mentioned together in the same context， for example， the same sentence， with the assumption that textual proximity implies biological association. The second approach is to use precise linguistic patterns based on NLP to find specific relationships between proteins. This can reveal the direction of the relationship and its nature such as “phosphorylation” or “upregulation”. The I2E system uses a flexible text-mining approach， supporting both of these strategies， as well as hybrid strategies which fall between the two. In this chapter we show how multiple strategies can be combined to obtain highquality results.

　　Key words: Protein–protein interactions， text mining， natural language processing， NLP， knowledge discovery， information extraction， linguistics， literature， MEDLINE， Linguamatics， I2E.

　　1. Introduction

　　Making effective use of published information is imperative to inform scientific decision making and prioritize investment of time and money. Text mining provides automated methods that can dramatically increase the speed at which relevant information can be extracted from text. The use of natural language processing (NLP) for text mining exploits the linguistic structure of text to extract its meaning. The extracted information can be presented in a structured format for more easy analysis， added to a database， or combined with numeric data sources for statistical analysis.

　　Information search and extraction was previously the domain of the information specialist but new tools have made text mining， including NLP-based technologies， accessible to a wider pool of users from scientists to business analysts， with results delivered directly to network visualizers， spreadsheets， or reporting tools to support decision making.

　　Text mining is sometimes seen as an alternative to expensive， hand-curated databases for obtaining structured information in a particular area. In practice， however， organizations may well invest in both technologies， as the two approaches can be highly complementary. Text mining can contribute to a mixed strategy in a number of ways including:

　　1. To fill the gaps in existing databases. For example， to find relationships from a compound to a disease through interaction with a protein， a database of compound–protein interactions might be combined with relationships from proteins to diseases from text mining.

　　2. To add information from internal documents where hand curation of those documents can be prohibitively expensive.

　　3. To provide additional information to that identified by hand curation. Databases may be designed to include very comprehensive information from a set of the most relevant documents，but can miss information from the wider set of documents (3).

　　4. To provide extra context for information. When building a database， decisions are made a priori on the contextual information that will be recorded for each relationship. However， for a particular task， other contextual information may be of interest. Text mining can be used to find contextual information which was in the original text but not captured in the database， e.g.， a binding constant or an experimental parameter.

　　5. To provide timely results directly from newly published documents (or news feeds).

　　NLP-based text mining using linguistics is now used in a large variety of applications within the life science domain， including detecting and examining gene–disease relationships， compound profiles， adverse events， patent information， market intelligence， etc. However， much of the early work was in the area of finding protein–protein interactions from the literature (e.g.， (4， 5)). Within a single abstract， or even in a sentence， there can be a large number of proteins in a variety of different kinds of relationship. Looking for proteins that occur together in the same context cannot distinguish between， for example， “sisterhood relationships” such as A-Raf and B-Raf both being Raf proteins， or “interaction relationships” such as Raf interacting with Mek. The use of linguistic constructions ensures that the results are restricted to interaction relationships between the proteins of interest and determines the direction of the relationship， for exam

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编辑推荐

蛋白质,基因组,研究,英文

书籍介绍

从组学的生物时代开始，科学家一直追求的是降低基因组规模实验的复杂性，以便于了解其蕴含的基本生物学原理。在《蛋白质网络与途径分析》这本书中，专家从业人员汇编了函数数据分析的方法，经常被称为系统生物学，它被应用于药物研发、医学和基础医学领域的研究中。本书分为三部分：1)对蛋白质、化合物和基因之间相互作用的阐述；2)介绍了网络、相互作用组和本体论研究中常用的分析工具；3)函数分析的应用范围。作为非常著名的《分子生物学方法》系列丛书之一，本书提供了详细的说明，并且为动手实践提供了建议。

权威和前沿的《蛋白质网络与途径分析》既阐明了生物实验室实验方法，又介绍了相关计算工具，涵盖了这个令人着迷的新兴领域中大多数的问题。本书的作者(巴斯)Nikolsky博士，GeneGo公司总裁，在生命科学领域有着数十年的工作经验。