Sentence examples for genomic interactions associated from inspiring English sources

Herein we proposed a novel approach for identifying knowledge-driven genomic interactions associated with cancer stage using GENN.

In this study, we demonstrate a novel approach for identifying knowledge-driven genomic interactions associated with cancer stage using grammatical evolution neural networks as part of the Analysis Tool for Heritable and Environmental Network Associations ATHENAA) software [ 18].

We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information.

Thus, the questions of how the chromatin organization changes upon environmental stimulation such as hormone and chemicals and how these genomic interactions are associated with disease development and progression remains elusive.

Lately, other genomic-scale methods, such as cDNA-AFLP, serial analysis of gene expression, cDNA microarray, and proteomics have been developed to study the interactions associated with plant-pathogens [ 30].

Further, we integrated the KEGG pathway-based matrix, GO-based matrix, and Pfam-based matrix in order to identify knowledge-driven genomic interactions between different biological knowledge sources associated with stage in ovarian cancer.

Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN).

Such datasets combined with increased knowledge on genome regulatory elements and long genomic interactions deposited in the ENCODE database [ 5] could potentially lead to the identification of genes whose expression changes are associated with altered H3K27Ac status.

Even though models from miRNA and CNA data showed additive effects, the models from methylation and gene expression data showed complex and non-linear interactions between genomic features associated with survival.

She hasn't, though, solved the problem of how to make the mind-numbing complexity of some genomic interactions and the confusing nomenclature of genes palatable to the general reader.

Here, we reported a regulatory inference model of epigenomic and genomic interactions.