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Querying Large Collections of Semistructured Data
Querying Large Collections of Semistructured Data

... to distinguish text documents and rank them, math symbols do not contain much semantic information on their own. Unfortunately, considering the structure of mathematical expressions to calculate relevance scores of documents results in ranking algorithms that are computationally more expensive than ...
Rfam Documentation
Rfam Documentation

Dynamic Programming: Edit Distance
Dynamic Programming: Edit Distance

BLAST - UCSD CSE
BLAST - UCSD CSE

Using extended feature objects for partial similarity
Using extended feature objects for partial similarity

... In the literature, there is a lot of work on similarity search of geometry data. In computational geometry, researchers focus on the theoretical aspects of the 1 : 1 similarity problem. Most of the proposed algorithms are based on similarity measures inadequate for our application [AB 92]. Another a ...
CS 372: Computational Geometry Lecture 14 Geometric
CS 372: Computational Geometry Lecture 14 Geometric

Additional file 1
Additional file 1

... each structure gene’s promoter sequences (multiple times appeared in one sequence is regarded as one appearance). “CACGTG” represents oligos which have at most one nucleotide mismatch with “cacgtg” (all possible oligos are 4X6); “xxxx” represents oligos composed by 4 nucleotide (all possible oligos ...
Non-Negative Matrix Factorization Revisited: Uniqueness and
Non-Negative Matrix Factorization Revisited: Uniqueness and

Stochastic Search and Surveillance Strategies for
Stochastic Search and Surveillance Strategies for

mixture densities, maximum likelihood, EM algorithm
mixture densities, maximum likelihood, EM algorithm

Genome Rearrangements ()
Genome Rearrangements ()

phylogenetic tree
phylogenetic tree

Unipro UGENE Manual
Unipro UGENE Manual

Unipro UGENE Manual
Unipro UGENE Manual

Annotation Strategy Guide - GEP Community Server
Annotation Strategy Guide - GEP Community Server

... Zooming in on this region, we can see there is some support for 54,810 (where the tblastn alignment terminates) as the end coordinate for the first exon (Figure 3). However, looking a little further downstream, you can see there is much stronger support to place the end of the exon at 54,816. While ...
Bioinformatics - cs@union
Bioinformatics - [email protected]

Analysis of Cross Sequence Similarities for Multiple - PolyU
Analysis of Cross Sequence Similarities for Multiple - PolyU

... In Figure 2(a), a set of 12 nucleotides ‘ACGCTTACGCAT’ is a sample sequence. The subsequence ‘ACGCTT’ shown between 1 and 6 indicates the first six bases of the sample sequence while the subsequence ‘ACGCAT’ listed between 7 and 12 is the 7th to 12th bases of the sample sequence. The vertical line l ...
Searching for Compact Hierarchical Structures in DNA by means of
Searching for Compact Hierarchical Structures in DNA by means of

GENtle, a free multi-purpose molecular biology tool
GENtle, a free multi-purpose molecular biology tool

... information. This trend notwithstanding, general purpose software for these tasks often suffers from severe drawbacks. Free software exists, but is often hard to set up and operate for users on today’s point-and-click interfaces, and usually leads to the application of a patch-work of multiple, only ...
a genetic algorithm for the automatic generation of
a genetic algorithm for the automatic generation of

... it is not the only concern. In cases where notes can be placed on the fretboard in multiple positions without significant differences in playability, the position chosen by a professional could seem essentially arbitrary. Because each guitar string has a slight but noticeable difference in timbre, t ...
Aalborg Universitet Trigonometric quasi-greedy bases for Lp(T;w) Nielsen, Morten
Aalborg Universitet Trigonometric quasi-greedy bases for Lp(T;w) Nielsen, Morten

... x ∈ X, An (x) is a linear combination of at most n elements from B. We say that the algorithm is convergent if limn→∞ kx − An (x)kX = 0 for every x ∈ X. For a Schauder basis there is a natural convergent approximation algorithm. Suppose the dual system to ...
BLAST - Mark Goadrich
BLAST - Mark Goadrich

... An Introduction to Bioinformatics Algorithms ...
wsp Gene Sequences from the Wolbachia of Filarial Nematodes
wsp Gene Sequences from the Wolbachia of Filarial Nematodes

... The PCR products obtained with primers WSPintF and WSPintR from nematode Wolbachia were sequenced directly, and the sequences were aligned to the wsp gene available for arthropod Wolbachia. We also tried to align wsp to the gene sequences available for the major outer membrane proteins of Anaplasma, ...
Selecting Degenerate Multiplex PCR Primers
Selecting Degenerate Multiplex PCR Primers

Analysis of expressed sequence tags from the Huperzia serrata leaf
Analysis of expressed sequence tags from the Huperzia serrata leaf

... tetraketides (Morita et al. 2007, Wanibuchi et al. 2007). However, as of January 2009, there were only 10 nucleotide sequences from H . serrata available in the NCBI database. The limited information on the genetic contents of this plant triggered our efforts to construct a cDNA library from the H . ...
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Smith–Waterman algorithm

The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. Like the Needleman–Wunsch algorithm, of which it is a variation, Smith–Waterman is a dynamic programming algorithm. As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap-scoring scheme). The main difference to the Needleman–Wunsch algorithm is that negative scoring matrix cells are set to zero, which renders the (thus positively scoring) local alignments visible. Backtracking starts at the highest scoring matrix cell and proceeds until a cell with score zero is encountered, yielding the highest scoring local alignment. One does not actually implement the algorithm as described because improved alternatives are now available that have better scaling (Gotoh, 1982) and are more accurate (Altschul and Erickson, 1986).
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