本書的目錄和前言已經譯成中文，正文部分保留英文原版。另附中國醫(yī)科院基礎醫(yī)學研究所博士生導師高友鶴教授所作導讀一篇。
從組學的生物時代開始，科學家一直追求的是降低基因組規(guī)模實驗的復雜性，以便于了解其蘊含的基本生物學原理。在《蛋白質網絡與途徑分析》這本書中，專家從業(yè)人員匯編了函數(shù)數(shù)據(jù)分析的方法，經常被稱為系統(tǒng)生物學，它被應用于藥物研發(fā)、醫(yī)學和基礎醫(yī)學領域的研究中。本書分為三部分：1）對蛋白質、化合物和基因之間相互作用的闡述；2）介紹了網絡、相互作用組和本體論研究中常用的分析工具；3）函數(shù)分析的應用范圍。作為非常著名的《分子生物學方法》系列叢書之一，本書提供了詳細的說明，并且為動手實踐提供了建議。
權威和前沿的《蛋白質網絡與途徑分析》既闡明了生物實驗室實驗方法，又介紹了相關計算工具，涵蓋了這個令人著迷的新興領域中大多數(shù)的問題。
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《蛋白質網絡與途徑分析》由來自于歐美的學術領域、政府研究機構、醫(yī)藥產業(yè)和生物信息學公司的一線從業(yè)人員撰寫，內容具有專業(yè)性和前沿性，全面廣泛地介紹了系統(tǒng)生物學實驗和函數(shù)數(shù)據(jù)分析的具體分析方法。本書的內容分為三個部分。第一部分闡明蛋白質、化合物和基因問的相互作用以及蛋白質相互作用的手工注釋。第二部分是關于函數(shù)分析的專業(yè)工具介紹。第三部分介紹了函數(shù)分析的應用。本書的作者(巴斯)Nikolsky博士，GeneGo公司總裁，在生命科學領域有著數(shù)十年的工作經驗。
目錄
前言 v
撰稿人 ix
第一部分：相互作用
1. 用Linguamatics公司研發(fā)的I2E軟件從發(fā)表的文獻中挖掘蛋白質相互作用 3
JudithBandy，DavidMilward，andSarahMcQuay
2. 基因組規(guī)模實驗中轉錄因子與DNA結合的相對親和力、特異性和敏感度 15
VladimirA.Kuznetsov
3. 抑制因子-靶標數(shù)據(jù)的管理：步驟和在途徑分析上的作用 51
SreenivasDevidas
4. 用功能蛋白質芯片描繪蛋白質相互作用網絡 63
DawnR.MattoonandBarrySchweitzer
5. 蛋白質相互作用的手工注釋 75
SvetlanaBureeva，SvetlanaZvereva，ValentinRomanov，and TatianaSerebryiskaya
第二部分：分析
6. 基因集富集分析 99
CharlesA.TilfordandNathanO.Siemers
7. PANTHER途徑：一個整合了數(shù)據(jù)分析工具且基于本體的途徑數(shù)據(jù)庫 123
HuaiyuMiandPaulThomas
8. 采用網絡分析優(yōu)化排序影響途徑的基因 141
AaronN.Chang
9. 從多樣的功能基因組數(shù)據(jù)中發(fā)掘生物學網絡 157
ChadL.Myers，CameliaChiriac，andOlgaG.Troyanskaya
10. 在基于知識的集成平臺上對組學數(shù)據(jù)及小分子化合物的函數(shù)分析 177
YuriNikolsky，EugeneKirillov，RomanZuev，EugeneRakhmatulin，and TatianaNikolskaya
11. 動力學模型作為一種整合多層次動態(tài)實驗數(shù)據(jù)的工具 197
Ekaterina Mogilevskaya，Natalia Bagrova，Tatiana Plyusnina，Nail Gizzatkulov，Eugeniy Metelkin，EkaterinaGoryacheva，SergeySmirnov，YuriyKosinsky，AleksanderDorodnov，KirillPeskov，TatianaKarelina，IgorGoryanin，andOlegDemin
12. Cytoscape：用于網絡建模的一個基于社區(qū)的框架 219
SarahKillcoyne，GregoryW.Carter，JenniferSmith，andJohnBoyle
13. 用語義數(shù)據(jù)集成和知識管理表示生物網絡相關性 241
SaschaLoskoandKlausHeumann
14. 復雜的、多數(shù)據(jù)類型及多工具分析的解決方案：運用工作流程與流水線方法的 原則及應用 259
RobinE.J.MunroandYikeGuo
第三部分：應用
15. 高通量siRNA篩選結合化合物篩選作為一種干擾生物系統(tǒng)以及識別目標途徑的方法 275
JeffKiefer，HongweiH.Yin，QiangQ.Que，andSpyroMousses
16. 用高密度等位基因關聯(lián)數(shù)據(jù)進行途徑和網絡的分析 289
AliTorkamaniandNicholasJ.Schork
17. miRNAs：從生物起源到網絡 303
GiuseppeRussoandAntonioGiordano
18. MetaMiner(CF)：疾病導向的生物信息學分析環(huán)境 353
JerryM.Wright，YuriNikolsky，TatianaSerebryiskaya，andDianaR.Wetmore
19. 轉化研究與生物醫(yī)學信息學 369
MichaelLiebman
20. ArrayTrack：一個美國食品及藥物管理局(FDA)和公共基因組工具 379
HongFang，StephenC.Harris，ZhenjiangSu，MinjunChen，F(xiàn)eng Qian，Leming Shi，RogerPerkins，andWeidaTong
索引 399
(高友鶴 尹劍銳 譯)
Contents
Preface v
Contributors ix
SECTION I：INTERACTIONS
1. Mining Protein–Protein Interactions from Published Literature Using Linguamatics I2E 3
Judith Bandy，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，Aleksander 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，F(xiàn)eng Qian，Leming Shi，Roger Perkins，and Weida Tong
Index 399
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
(www.linguamatics.com (2)).
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 relationshi
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