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Our tool supports a range of complex analyses, including – among others – low- and high-level analyses of DNA microarray data, integrated analysis of transcriptomics and lipidomics data and the ability to infer miRNA-mRNA interactions.
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The table shows the amount of microarray expression data integrated into TRUNCATULIX via EMMA.
Microarray data was integrated into the network using the median intensity value as the node weights in the network.
To make the network more biologically significant, microarray data was integrated into the network as node weights.
However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources.
Our parameter estimation algorithm also provides relatively high-quality optimization for the fit between model and microarray data, by integrating both global and local cost minimization techniques, in contexts where plausible ranges of values for most of the unknown parameters are not available in the literature.
Furthermore, the plant microarray data will be integrated into "Gene group analysis" of PlantPAN.
Numerous applications to conduct statistical analysis on DNA microarray data have been integrated into BBGD for rapid data analysis.
Based on EST, protein profile and microarray data, we performed integrated data mining to uncover signature embolic pathways in the endosperm.
An important component of this approach is to ensure that in situ and microarray data are fully integrated for interrogation and analysis.
Overall, regardless of the data type used, whether we use microarray data alone or integrated data from microarrays and ChIP-seq, the number of genes predicted to be MSMG from the positive test set is significantly higher than from the negative test set (p-value ≈ 5.34 × 10-12, two-tail t-test).
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