Intelligent Strategies for Pathway Mining

Model and Pattern Identification

  • Qingfeng Chen
  • Baoshan Chen
  • Chengqi Zhang

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8335)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 8335)

Table of contents

  1. Front Matter
  2. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 1-43
  3. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 45-59
  4. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 61-83
  5. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 85-105
  6. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 107-126
  7. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 127-150
  8. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 151-173
  9. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 175-192
  10. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 193-217
  11. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 219-233
  12. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 235-253
  13. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 255-270
  14. Qingfeng Chen, Baoshan Chen, Chengqi Zhang
    Pages 271-277
  15. Back Matter

About this book


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.


association rule mining bioinformatics comparative genomics gene regulation protein structure prediction

Editors and affiliations

  • Qingfeng Chen
    • 1
  • Baoshan Chen
    • 1
  • Chengqi Zhang
    • 2
  1. 1.School of Computer, Electronic and Information, State Key laboratory for Conservation and Utilization of Subtropical Agro-BioresourcesGuangxi UniversityNanningChina
  2. 2.Centre for Quantum Computation and Intelligent SystemsUniversity of Technology SydneyBroadwayAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-04171-1
  • Online ISBN 978-3-319-04172-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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