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Titlebook: Data Mining in Bioinformatics; Xindong Wu,Lakhmi Jain,Dennis Shasha Book 2005 Springer-Verlag London 2005 Alignment.Markov chain.XML.bioin

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https://doi.org/10.1007/b138131Alignment; Markov chain; XML; bioinformatics; biology; calculus; classification; clustering; computer; data a
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978-1-84996-894-2Springer-Verlag London 2005
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https://doi.org/10.1007/978-3-031-41837-2d on them to make new discoveries on his or her own. The book contains twelve chapters in four parts, namely, overview, sequence and structure alignment, biological data mining, and biological data management. This chapter provides an introduction to the field and describes how the chapters in the b
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https://doi.org/10.1007/978-3-031-41837-2 homologous sequences belonging to classes generated by some clustering algorithm and then continuing the alignment process in a bottom-up way along a suitable tree structure. The final result is then read at the root of the tree. Multiple sequence alignment in each cluster makes use of progressive
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https://doi.org/10.1007/978-3-031-41837-2e or a base pair in the RNA molecule. With this structural representation scheme, we give efficient algorithms for computing the distance and alignment between two RNA secondary structures based on edit operations and on the assumptions in which either no bond-breaking operation is allowed or bond-b
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https://doi.org/10.1007/978-3-031-41837-2ethod provides posterior distributions on the number of segments in the data and thus gives a much broader view on the potential data than do methods (such as dynamic programming) that aim only at finding a single optimal solution. On the other hand, MCMC methods can be more difficult to implement t
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