Sentence examples for both gene coexpression data from inspiring English sources
After applying these measures to generate a synthetic network, we searched for likely connections between Apc and Cdkn1a using both gene coexpression data and Gene Ontology association rules.
However, this estimation is not robust enough and a proper calculation of the statistical "accuracy" and "coverage" along all the gene coexpression data matrices was done.
This can be expected from the mere definition of house-keeping; however, since the result is obtained by mapping external datasets [16], [17] on our human gene coexpression data, it provides functional validity to our coexpression study.
In a second approach, we investigate the functional assignment of the gene coexpression data following the strategy taken by Stuart et al. [5], who explored functional coverage on a coexpression network obtained for four organisms looking at the percentage of genes that are connected to at least one other gene in the same "functional category".
The focus of our assessment was to obtain more information from gene coexpression data.
Our results provide a standard calculation procedure for condition-independent gene coexpression data to elucidate gene-to-gene functional relationships.
To easily extract pertinent information from gene coexpression data, gene coexpression databases with various analysing tools have been constructed for model organisms.
To calculate condition-independent gene coexpression data, we constructed gene expression profiles using as many genes and samples as possible.
MRFSeq [ 12] combines a Markov random field (MRF) model and the gene coexpression data to predict differential gene expression.
The STRING database integrates experimentally verified PPIs with additional data sources, including genomic context, gene coexpression data and text-mined data.
In fact, gene coexpression data are extensively used for gene targeting in Arabidopsis research, whereas this is not a major approach used in mammalian studies.
