Sentence examples for significantly better alignment from inspiring English sources

Although CLUSTALW is still the most popular alignment tool to date, recent methods offer significantly better alignment quality and, in some cases, reduced computational cost.

At the distal region, the lateral cortical surface gives significantly better alignment compared to the medial cortical surface (p < 0.01), but not as accurate (1.4°) as in the central region.

Students in the upper quartile demonstrated significantly better alignment between perception and determined knowledge than students in the lower quartile (χ 4, n = 384) = 51.848, p < 0.0001).

This sentence has been changed to read: Students in the upper quartile demonstrated significantly better alignment between perception and determined knowledge than students in the lower quartile on the posttest (χ 7, n = 479) = 74.163, p < 0.0001).

Page 325, column 2, first paragraph, fourth sentence: Students in the upper quartile demonstrated significantly better alignment between perception and determined knowledge than students in the lower quartile (χ 4, n = 384) = 51.848, p < 0.0001).

Students whose determined knowledge was in the upper quartile had significantly better alignment between their perception and determined knowledge on the pre- and posttest than students in the lower quartile.

Our new heuristic produces significantly better alignments, especially on globally related sequences, without increasing the CPU time and memory consumption exceedingly.

Experimental results confirm that our method generates significantly better alignments and threading results than the best profile-based methods on several large benchmarks.

Due to several algorithmic improvements, it produces significantly better alignments on locally and globally related sequence sets than previous versions of DIALIGN.

The use of structural information by both MAPPIS and CMAPi leads to significantly better alignments when compared to MUSCLE, which uses only sequence information.

Experimental results show that our context-specific alignment potential is much more sensitive than the widely used context-independent (e.g. profile-based) scoring function and yields significantly better alignments and threading results.