Sentence examples for biological time series data from inspiring English sources

Thus, we propose in the following the adaptation of statistical techniques to the significance analysis of differences between relatively sparse biological time series data.

The performance of the proposed algorithm was evaluated using both synthetic time series data sets and a biological time series data set for the yeast Saccharomyces cerevisiae.

For this reason, the recent years have seen numerous methods for matching S-system models to measured biological time series data.

Generally speaking – when modelling biological time series – data features that cannot be produced by a plausible forward model are probably measurement noise or the product of hidden processes not included explicitly in the model.

Although LSA had its roots grounded in microbial community analysis, the technique can be readily applied to other biological time series data, such as replicated gene expression time series data from microarray and RNA-Seq experiments [ 27- 29].

The estimation of parameter values from biological time series data is not a trivial problem, and the estimation algorithms for nonlinear mixed-effects are computationally complex and often provide less accurate inference than for linear effects models.

We applied two single biological condition time series data which are interested in exploring developmental transient patterns during a time period rather than timing difference patterns incorporated with multiple conditions at a time as Section 3.2.1 example.

Until recently, Bayesian model comparison approaches such as Bayes factors have largely been ignored in analysing the identifiability of biological systems from time series data [ 14].

In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data.

This was also true in the case of reverse engineering of gene regulatory networks from biological time series DNA microarray data.

A SOM is an artificial neural network, which is capable for the automated recognition of patterns within measurements and is well-suited for the categorization of time series data of biological entities.