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We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context.
Ma et al. [18] prioritized genes by combining gene expression and protein-protein interaction data for Alzheimer's disease.
Since transcription factor and miRNA share a common logic in gene expression regulation [34], in this paper we revise the method and apply it to infer the relative activities of miRNAs by combining gene expression profiling with miRNA target predictions.
By combining gene expression analyses using 70-mer oligonucleotide 16.4 K microarrays for both wild type symbionts and nodulation defective symbiotic mutants, we were able to identify and classify more than 3,400 differentially regulated genes and associated regulators.
Based on these studies, establishing a robust inference regarding the regulatory relationships between a certain transcription factor and its putative target gene(s) could be better accomplished by combining gene expression data with information on transcription factor binding sites and the possible types of interaction based on existing biological knowledge [9].
This was done by combining gene expression data from Affymetrix focus arrays with array CGH data.
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Some of these researchers have defined tissue-specific enhancers by combining gene-expression profiling, genome comparison, and TFBS analyses [ 18] or have predicted TF synergy using the relative position and co-occurrence of TFBSs in the promoters of genes expressed only in a particular tissue [ 19].
By using this combined gene expression and ALK fusion detection strategy, we developed a multiplexed assay with a quantitative scoring modality that is highly sensitive, reproducible, and capable of detecting low-abundant ALK fusion transcripts, even in samples with a low tumor cell content.
The method provided by this paper combined gene expression profiles and promoter sequences in a novel way.
A study by Tzika et al. combined gene expression microarrays and HR MAS MRS on the same sample of brain tumor and control biopsies [ 30].
Here, by combining gene network ideas with differential expression, the network components involving differentially expressed immunogenes are considered to participate in, or at least be tightly associated with, the biological process of H. parasuis infection.
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by combining gene coexpression
by combining microarray expression
by analyzing gene expression
by altering gene expression
by regulating gene expression
by combining protein expression
by comparing gene expression
by combining gene gene-expression
by optimizing gene expression
by influencing gene expression
by combining gene duplication
by combining gene gene
by combining gene prediction
by modulating gene expression
by prolactin gene expression
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