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024 7 _a10.1007/978-981-99-8251-6
_2doi
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082 0 4 _a006.312
_223
100 1 _aChen, Qingfeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9101067
245 1 0 _aAssociation Analysis Techniques and Applications in Bioinformatics
_h[electronic resource] /
_cby Qingfeng Chen.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXXI, 388 p. 116 illus., 58 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _aChapter1: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.
520 _aAdvances 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. .
650 0 _aData mining.
_93907
650 0 _aExpert systems (Computer science).
_93392
650 0 _aMachine learning.
_91831
650 0 _aBioinformatics.
_99561
650 0 _aBig data.
_94174
650 0 _aMedical informatics.
_94729
650 1 4 _aData Mining and Knowledge Discovery.
_9101071
650 2 4 _aKnowledge Based Systems.
_979172
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputational and Systems Biology.
_931619
650 2 4 _aBig Data.
_94174
650 2 4 _aHealth Informatics.
_931799
710 2 _aSpringerLink (Online service)
_9101075
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819982509
776 0 8 _iPrinted edition:
_z9789819982523
776 0 8 _iPrinted edition:
_z9789819982530
856 4 0 _uhttps://doi.org/10.1007/978-981-99-8251-6
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