Chengqi Zhang

This book is organized into thirteen chapters that range over the relevant approaches and tools in data integration, modeling, analysis and knowledge discovery for signaling pathways. Having in mind that the book is also addressed for students, the contributors present the main results and techniques in an easily accessed and understood way together with many references and instances. Chapter 1 presents an introduction to signaling pathway, including motivations, background knowledge and relevant data mining techniques for pathway data analysis. Chapter 2 presents a variety of data sources and data analysis with respect to signaling pathway, including data integration and relevant data mining applications. Chapter 3 presents a framework to measure the inconsistency between heterogenous biological databases. A GO-based (genome ontology) strategy is proposed to associate different data sources. Chapter 4 presents identification of positive regulation of kinase pathways in terms of association rule mining. The results derived from this project could be used when predicting essential relationships and enable a comprehensive understanding of kinase pathway interaction. Chapter 5 presents graphical model-based methods to identify regulatory network of protein kinases. A framework using negative association rule mining is introduced in Chapter 6 to discover featured inhibitory regulation patterns and the relationships between involved regulation factors. It is necessary to not only detect the objects that exhibit a positive regulatory role in a kinase pathway but also to discover those objects that inhibit the regulation. Chapter 7 presents methods to model ncRNA secondary structure data in terms of stems, loops and marked labels, and illustrates how to find matched structure patterns for a given query. Chapter 8 shows an interval-based distance metric for computing the distance between conserved RNA secondary structures. Chapter 9 presents a framework to explore structural and functional patterns of RNA pseudoknot structure according to probability matrix. Chapter 10 presents methods to model miRNA data and identify miRNA interaction of cross-species and within-species. Chapter 11 presents an approach to measure the importance of miRNA site and the adjacent base by using information redundancy and develops a novel measure to identify strongly correlated infrequent itemsets. The discover association rules not only present important structural features in miRNAs, but also promote a comprehensive understanding of regulatory roles of miRNAs. Chapter 12 presents bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets, and describes their potential application in pharmaceutical industry. Chapter 13 presents a summary of the chapters and give a brief discussion to some emerging issues.

65 x 39 = 2535108 x 312 = 3369627 x 84 = 2268Can you work out these calculations in your head? Many of us would say "No way " For anyone who reads this book and learns the tricks it contains, it won't be difficult anymore.This book aims to stimulate enthusiasm for math through rapid calculation, thereby creating a solid mathematical foundation for further learning. The goal is to express profound mathematical ideas in simple language, conveying the magic and mystery of calculation. As long as you can multiply two-digit numbers, do addition and subtraction, and understand simple negative numbers, you can handle the basics of this book.

Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy,and ub- uitouscomputingandnetworkingacrosseverysectorand business,data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the ?ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications. The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au­ thors who have developed a local pattern analysis, a new strategy for dis­ covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv­ ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe­ culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter­ esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis­ tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy,and ub- uitouscomputingandnetworkingacrosseverysectorand business,data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the ?ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications. The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.

The application of formal methods to security protocol analysis has attracted increasing attention in the past two decades, and recently has been sh- ing signs of new maturity and consolidation. The development of these formal methodsismotivatedbythehostilenatureofsomeaspectsofthenetworkand the persistent e?orts of intruders, and has been widely discussed among - searchers in this ?eld. Contributions to the investigation of novel and e?cient ideas and techniques have been made through some important conferences and journals, such asESORICS,CSFW andACM Transactions in Computer Systems. Thus, formal methods have played an important role in a variety of applications such as discrete system analysis for cryptographic protocols, - lief logics and state exploration tools. A complicated security protocol can be abstractedasamanipulationofsymbolsandstructurescomposedbysymbols. The analysis of e-commerce (electronic commerce) protocols is a particular case of such symbol systems. There have been considerable e?orts in developing a number of tools for ensuring the security of protocols, both specialized and general-purpose, such as belief logic and process algebras. The application of formal methods starts with the analysis of key-distribution protocols for communication between two principals at an early stage. With the performance of transactions - coming more and more dependent on computer networks, and cryptography becoming more widely deployed, the type of application becomes more varied and complicated. The emerging complex network-based transactions such as ?nancial transactionsand secure groupcommunication have not only brought innovationstothecurrentbusinesspractice,butthey alsoposeabigchallenge to protect the information transmitted over the open network frommalicious attacks.

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au­ thors who have developed a local pattern analysis, a new strategy for dis­ covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv­ ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe­ culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter­ esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis­ tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention.The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.