Sentence examples for models from gene from inspiring English sources

A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data.

A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data.

As the amount of gene expression data is growing, researchers are becoming increasingly interested in the automated inference or reverse-engineering of quantitative dynamic, mechanistic gene-regulatory network models from gene expression time-course data [ 5, 4, 1- 9].

We believe this is an important contribution, as it will help scientists to understand better the relationship between formalisms used to represent GRN models and automated procedures that generate such models from gene expression data.

The remainder of this paper is organized as follows: We first present two common semi-mechanistic mathematical models used to represent GRN systems, and an algorithm to automatically infer (reverse-engineer) such models from gene-expression time-course data.

Reconstruction of gene regulatory network is a reverse engineering to infer all of the unknown parameters in linear model from gene expression data.

Many approaches are proposed for gene regulatory networks modeling from gene expression data, such as Boolean network [ 3- 6], linear model [ 7- 9], Bayesian networks [ 10- 14], neural networks [ 15, 16], differential equations [ 17- 19], models including stochastic components on the molecular level [ 20], and so on.

Altogether, there were 57,079 gene models from 51,515 genes that could be annotated with a protein sequence of at least 30 residues, the expected lower range of protein sizes based on Arabidopsis annotations.

Using custom python scripts, we then compared the inferred intron/exon boundaries in the ESTs with the overlapping gene models from the TAIR9 gene structure annotations.

Amino acid sequences of 1,047,986 gene models from the complete gene sets of all 36 species were used as input to OrthologID, which produced 26,612 orthologous groups with at least 4 species represented.

Biofilter utilizes biologically validated knowledge of the relationships between sets of genes to build pairwise SNP-SNP models from functionally linked gene-gene pairs.