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Atom mapping algorithms use different chemical formats to input and output atom mappings.
The short read mapping algorithms use edit distance and other read characteristics to compute a mapping quality score for each mapped read (Li et al., 2008).
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The optimization procedure is analyzed with simulated annealing, group migration and random mapping algorithms using test graphs from the Standard Task Graph Set.
To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated.
In order to test this premise, we designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR).
In addition, there is some variation in the QTL mapping algorithms used in different studies.
Both reviews, however, were concerned by the quality of these mapping algorithms using OLS.
Critically, BLAT works well over a range of reads lengths, whereas mapping algorithms used by other fusion finders are optimised for short reads.
In this paper, we present a benchmark procedure to compare mapping algorithms used in HTS using both real and simulated datasets and considering four evaluation criteria: computational resource and time requirements, robustness of mapping, ability to report positions for reads in repetitive regions, and ability to retrieve true genetic variation positions.
Hash-based genome-mapping algorithms use hash tables.
This paper proposes a two-stage table mapping algorithm using arithmetic operations with one condition.
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