Sentence examples for molecular connectivity maps from inspiring English sources
The concept of molecular connectivity maps is gaining an increased popularity in systems chemical biology [27], which can help researchers to study and compare the molecular therapeutic/toxicology profiles of many candidate drugs.
The systematic large-scale analyses of 124 human diseases create three classes of molecular connectivity maps.
The concept of molecular connectivity maps has been proposed to establish comprehensive knowledge links between molecules of interest in a given biological context.
In post-genome biology, molecular connectivity maps have been proposed to establish comprehensive knowledge links between molecules of interest in a given biological context [ 14].
Molecular connectivity maps between drugs and genes/proteins in a disease-specific context can be particularly valuable because they allow researchers to evaluate drugs against each other using their unique gene/protein-drug association profiles.
Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic profiles and toxicological profiles of candidate drugs.
The molecular connectivity map shows how the expression level of genes change in response to different drug compound perturbations, which enables researchers to compare the molecular therapeutic/toxicological profiles of many candidate drugs or drug target genes, therefore improving the chance of developing high quality drugs and reducing drug development time.
One approach for developing molecular connectivity map data is to generate disease-specific protein-drug association profiles computationally by mining bio molecular interaction networks and PubMed literature [ 15].
We showed that this approach was effective in constructing a statistically significant molecular connectivity map of 124 diseases with 3632 proteins.
Systems biological approach of molecular connectivity map has reached to a great interest to understand the gene functional similarities between the diseases.
When querying a database of small molecular inhibitors (Connectivity Map) using expression data from our microarray set, two HDAC inhibitors and the PI3K-inhibitor Wortmannin were the top-ranked compounds significantly (P<0.01) anti-correlated with the ER α+/GPER− gene signature (Table 3).
