Overview
- Provides essential information on both theoretical and practical aspects of association analysis
- Includes case studies that illustrate the application of data mining in computational biology
- Offers readers valuable insight into the latest studies on big data analysis in biology
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About this book
This book offers an essential introduction to the theoretical and practical aspects of association analysis, including data pre-processing, data mining methods/algorithms, and tools that are widely applied for computational biology. It covers significant recent advances in the field, both foundational and application-oriented, helping readers understand the basic principles and emerging techniques used to discover interesting association patterns in diverse and heterogeneous biology data, such as structure-function correlations, and complex networks with gene/protein regulation.
The main results and approaches are described in an easy-to-follow way and accompanied by sufficientreferences and suggestions for future research. This carefully edited monograph is intended to provide investigators in the fields of data mining, machine learning, artificial intelligence, and bioinformatics with a profound guide to the role of association analysis in computational biology. It is also very useful as a general source of information on association analysis, and as an overall accompanying course book and self-study material for graduate students and researchers in both computer science and bioinformatics.Keywords
Table of contents (13 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Qingfeng Chen received his BSc and MSc degrees in Mathematics from Guangxi Normal University, China, in 1995 and 1998, respectively, and his PhD in Computer Science from the University of Technology Sydney in September 2004. He is currently a Professor at Guangxi University, China and an honorary research fellow at La Trobe University, Australia. He has published many papers in top venues for machine learning and artificial intelligence, serves as an associate editor for top journals including Complexity & Intelligent systems, and has co-chaired several international conferences.
Bibliographic Information
Book Title: Association Analysis Techniques and Applications in Bioinformatics
Authors: Qingfeng Chen
DOI: https://doi.org/10.1007/978-981-99-8251-6
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Guangxi Education Publishing House, Guangxi, China. 2024
Hardcover ISBN: 978-981-99-8250-9Published: 26 April 2024
Softcover ISBN: 978-981-99-8253-0Due: 27 May 2024
eBook ISBN: 978-981-99-8251-6Published: 25 April 2024
Edition Number: 1
Number of Pages: XXI, 388
Number of Illustrations: 58 b/w illustrations, 58 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Knowledge based Systems, Machine Learning, Bioinformatics, Big Data, Health Informatics