Journal ArticleDOI
Comparing multiple RNA secondary structures using tree comparisons
Bruce A. Shapiro,Kaizhong Zhang +1 more
TLDR
This paper presents another approach to the problem of comparing many secondary structures by utilizing a very efficient tree-matching algorithm that will compare two trees in O([T1] X [T2] X L1 X L2) in the worst case and very close to O[T1?] for average trees representing secondary structures.Abstract:
In a previous paper, an algorithm was presented for analyzing multiple RNA secondary structures utilizing a multiple string alignment algorithm. In this paper we present another approach to the problem of comparing many secondary structures by utilizing a very efficient tree-matching algorithm that will compare two trees in O([T1] X [T2] X L1 X L2) in the worst case and very close to O([T1] X [T2]) for average trees representing secondary structures. The result of the pairwise comparison algorithm is then used with a cluster algorithm to produce a multiple structure clustering which can be displayed in a taxonomy tree to show related structures.read more
Citations
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Proceedings ArticleDOI
Structural joins: a primitive for efficient XML query pattern matching
TL;DR: It is shown that, in some cases, tree-merge algorithms can have performance comparable to stack-tree algorithms, in many cases they are considerably worse, and this behavior is explained by analytical results that demonstrate that, on sorted inputs, the stack- tree algorithms have worst-case I/O and CPU complexities linear in the sum of the sizes of inputs and output, while the tree-MERge algorithms do not have the same guarantee.
Journal ArticleDOI
From Sequences to Shapes and Back: A Case Study in RNA Secondary Structures
TL;DR: Using an algorithm for inverse folding, it is shown that sequences sharing the same structure are distributed randomly over sequence space, which means that finding a particular structure by mutation and selection is much simpler than expected.
Journal ArticleDOI
RNA sequence analysis using covariance models.
Sean R. Eddy,Richard Durbin +1 more
TL;DR: This work describes a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family, called 'covariance models'.
Journal ArticleDOI
Dengue Virus Structural Differences That Correlate with Pathogenesis
Katrin C. Leitmeyer,David W. Vaughn,Douglas M. Watts,Rosalba Salas,Iris Villalobos,de Chacon,Celso Ramos,Rebeca Rico-Hesse +7 more
TL;DR: A method for comparing dengue type 2 genomes (reverse transcriptase PCR in six fragments) directly from patient plasma is developed to better reflect the true composition of viral RNA populations in the natural host and permit their association with pathogenesis.
Proceedings ArticleDOI
Efficiently mining frequent trees in a forest
TL;DR: This work presents TREEMinER, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list, and finds that TREEMINER outperforms the pattern matching approach by a factor of 4 to 20, and has good scaleup properties.
References
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Journal ArticleDOI
Pattern Matching in Trees
TL;DR: Five new techniques for tree pattern matching are presented, analyzed for time and space complexity, and compared with previously known methods.
Journal ArticleDOI
Code generation using tree matching and dynamic programming
TL;DR: A tree-manipulation language called twig has been developed to help construct efficient code generators that combines a fast top-down tree-pattern matching algorithm with dynamic programming.
Proceedings ArticleDOI
Extracting semantic hierarchies from a large on-line dictionary
TL;DR: Automatic and semi-automatic procedures for extracting and organizing semantic feature information implicit in dictionary definitions are described and two head-finding heuristics are described for locating the genus terms in noun and verb definitions.
Journal ArticleDOI
An improved algorithm for approximate string matching
Zvi Galil,Kunsoo Park +1 more
TL;DR: Given a text string, a pattern string, and an integer k, a new algorithm for finding all occurrences of the pattern string in the text string with at most k differences is presented.
Proceedings ArticleDOI
Faster tree pattern matching
TL;DR: The authors improve an O(nm/sup 0.75/ polylog(m)-step algorithm for tree pattern matching by designing a simple O(n square root m polylog (m) algorithm.
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