We gathered a large compendium of data related to sequence, RNA structure, conserved domains, sequence similarity, and functional annotation of Arabidopsis and soybean transcripts.
The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart).
In order to account for the dynamic properties of proteins as well as their dynamic relationship with their neighbors in the network, we used gene expression information from a large compendium of microarray data and a high quality collection of protein interaction data to derive 9 network metrics that describe the dynamic behavior of a protein and of its neighborhood in the network (see Methods).
Previous results by our group and others have shown that statistical analysis of gene expression profiles in a large compendium of expression data can predict targets of differentiation processes, and identify functionally coupled genes [ 12, 21, 22].
The coexpressed gene pairs that validated are shown here with their pathway annotations and IMP score, which can be interpreted as the predictive probability that the two genes have a functional interaction based on a large compendium of empirical data.
Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified.
Therefore, each edge is interpretable as the posterior probability, given a large compendium of empirical data collected from human-derived samples, that two genes work together to carry out a specific biological process.
The first of these looked at the associative correlations between miRNAs and the level of activation of a large compendium of pathways, inferred from gene-expression signatures.
We started from a large compendium of gene-expression experiments and inferred 878 putative regulatory interactions.
It unifies a large compendium of other published cancer microarray data, including Gene Expression Omnibus (GEO) [19] and Stanford Microarray Database (SMD) [20], and uniquely provides differential expression analyses comparing most major types of cancer with their respective normal tissues.
Additionally, it would be challenging to summarize the resulting genomic data efficiently, since the combinatorics of 30 developmental stages [ 5] by over 50 plant structures [ 6] makes a large compendium of predictions unwieldy as raw data.
