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By sequence alignment or pattern comparison, we found that there were sequences similar to EPIYA (such as EPIYT, ESIYT), although most sequences contained EPIYA.
However, by sequence pattern comparison, we found that EPIYA also belongs to segment C if it is followed by TIEE, TIDE, SIDD, TIDG, TIAE, or TIAD.
In order to facilitate the pattern comparison, we created three data structures as depicted in Figure 2. The first data structure stores list of edge patterns for each subgraph.
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To perform this pattern comparison automatically, we use techniques based on prediction error filters (PEFs) [18] to highlight differences between two images.
For the remaining six tests, which yield seven unique scores (the pattern comparison task produces two unique scores), we treated each cognitive test score as a repeat measure of global cognitive function in a random effects linear mixed model.
In order to address the pattern comparison challenge when analyzing the ABA database, we developed a robust image filtering method, dubbed histogram-row-column (HRC) algorithm.
Here we performed a meticulous expression pattern comparison of all known barhl fish paralogs and described a novel barhl paralog in medaka.
During this period, we found significant improvements in 3 subtests: finger tapping, pattern comparison reaction time, and memory.
The tracking algorithm is based on bit pattern comparison of pad data.
Possible applications of this computational method include pattern classification, cataloguing of ceramic coatings, creating databases of decorative patterns, creating pattern designs, pattern comparison between different cultures, tile cataloguing, and so on.
Figure 8 Gain pattern comparison.
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