04496nam a22006135i 4500 978-981-99-8251-6 DE-He213 20251030112012.0 cr nn 008mamaa 240425s2024 si | s |||| 0|eng d 9789819982516 978-981-99-8251-6 10.1007/978-981-99-8251-6 doi QA76.9.D343 UNF bicssc UYQE bicssc COM021030 bisacsh UNF thema UYQE thema Chen, Qingfeng. author. aut http://id.loc.gov/vocabulary/relators/aut 75786 Association Analysis Techniques and Applications in Bioinformatics [electronic resource] / by Qingfeng Chen. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXI, 388 p. 116 illus., 58 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter1:Computer science for Molecular biology -- Chapter2:Introduction to association analysis -- Chapter3:Introduction to computational linguistics and biology structure -- Chapter4:Matrix decomposition for dimensionality deduction -- Chapter5:Discovering conserved RNA secondary structures with structure similarity -- Chapter6:Gene ontology for non-coding RNAs classification -- Chapter7:Learning frequent sub-structure by graph mining -- Chapter8:Editing distance and its application to biology graph analytics -- Chapter9:Sequence assembly and applications -- Chapter10:Classifying protein structures by measuring structural similarity -- Chapter11:Identification of metabolic pathways with embedding network -- Chapter12:Emerging Knowledge integration-based approach with multi-sources data for bioinformatics -- Chapter13:Conclusion and Future Work. Advances in experimental technologies have given rise to tremendous amounts of biology data. This not only offers valuable sources of data to help understand biological evolution and functional mechanisms, but also poses challenges for accurate and effective data analysis. 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 sufficient references 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. . Data mining. 6941 Expert systems (Computer science). 67822 Machine learning. 55059 Bioinformatics. 64050 Big data. 57216 Medical informatics. 60693 Data Mining and Knowledge Discovery. Knowledge Based Systems. 67823 Machine Learning. 55059 Computational and Systems Biology. 65974 Big Data. 57216 Health Informatics. SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819982509 Printed edition: 9789819982523 Printed edition: 9789819982530 https://doi.org/10.1007/978-981-99-8251-6 ZDB-2-SCS ZDB-2-SXCS 59396 59396