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Since various aspects of the substitution gradients (e.g. slope, initiation point, saturation level) may well reflect important biological components of the replication process (e.g., rate of polymerization, initiation of replication, and single-stranded binding protein affinity, respectively), it is biologically important to obtain better analysis of these gradients and their evolution (4).

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Improvement of such gene annotations in quantity, coverage and quality is paramount to obtain better gene functional analysis results.

To obtain better time and frequency analysis results, we divide the audio processing procedure into two stages.

Alternatively, several spectra acquired from one cell can be combined in order to obtain better statistics for quantitative analysis.

Next, in order to obtain better performance, based on drawback analysis of the greedy approach, we propose a two-phase heuristic algorithm, in which the first phase is designed to generate an initial feasible solution and the second phase iteratively perfects the initial solution until no improvement can be made.

For all the results given in related tables in this study, one can consider applying Ward's or Median clustering methods and keep away from applying the Nearest clustering method for all distance measures to perform a hierarchical clustering analysis to obtain better results according to Rand's C statistics.

As it becomes increasingly practicable to analyze the genetic diversity of natural and experimental populations at high depth, we can hope to obtain better estimates from a detailed analysis of the signatures positive selection is expected to leave in such data.

Therefore, it will obtain better performance than other time-frequency analysis methods by combining FRFT transform and array signal processing to achieve DOA estimation of the wideband LFM signals.

The results show that the proposed scheme could obtain better performance when compared with several existing bilateral analysis schemes.

We thus turned to the analysis of sequence-based phylogenies to obtain better insights into their phylogenetic position.

Various analysis techniques, e.g., network motif analysis, centrality analysis, clustering analysis and model checking technique, will also be imported to the ASTD to obtain better understanding of systematic dynamics from simulation data as the future research.

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