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In the last three decades, technological advances have facilitated the generation of genetically engineered mouse models (GEMMs) to mimic the alterations frequently observed in human cancers or to conduct intervention studies and assess the relevance of candidate gene networks in tumor establishment, progression and maintenance.
We evaluated the relevance of candidate genes identified by this comparative seed-network approach in three ways.
Biological relevance of candidate genes was assessed using manual PubMed [http://www.ncbi.nlm.nih.gov/pubmed/] [107] searches with the following search terms: retina, retinal development, CNS development, brain development, development.
Gene expression is an important factor for judging the relevance of candidate genes.
No clear-cut connection can be made between the absolute sum of keyword values and the relevance of candidate genes.
To investigate this, the functional relevance of candidate genes has to be considered in a subtype-specific manner, taking MB heterogeneity into account.
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We also enhance the method by extending the structure of the inverted list and adopting a relevance lookup table, which enables more accurate estimation of the relevance scores of candidate root nodes and efficient search of top-k answer trees.
The learned tree-based model is used to derive an importance score for each candidate regulator, quantifying the relevance of that candidate regulator for the prediction of μ i (t ).
To investigate the biological relevance of the candidate genes correlated with the ESN, we examined the spatial and temporal expression of six candidate genes in the developing mouse retina.
In an online survey, 222 German-speaking participants rated the relevance of these candidate words in relation to food products.
This result proved the relevance of our candidate genes for studying late pollen development in japonica and indica rice.
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CEO of Professional Science Editing for Scientists @ prosciediting.com