Sentence examples for molecular networks using from inspiring English sources

The genes found to be modulated in response to C. burnetii infection were analyzed with molecular networks using pathway classification and web-based entry tools.

We generated molecular networks using a data set containing gene identifiers derived from a list of putative gene targets for each miRNA studied.

Concurrently, several studies have revealed the dynamics of small-scale molecular networks using innovative applications of reverse-engineering techniques, modeling and computer simulation of kinetic equations [ 2].

Functional interpretation of differentially expressed genes was analyzed in the context of gene ontology and molecular networks using the Ingenuity Pathways Analysis (IPA; Ingenuity Systems®; http://www.ingenuity.com).com

Analysis of differentially expressed genes was performed in the context of gene ontology, canonical pathways and molecular networks using the Ingenuity Pathways Analysis (IPA, Ingenuity Systems™, Qiagen, Redwood City, CA, USA).

Primer information is listed in Table 2. Functional interpretation of differentially expressed genes was analyzed in the context of gene ontology and molecular networks using the Ingenuity Pathways Analysis (IPA) 6.5 software (Ingenuity Systems®; http://www.ingenuity.com).com

Mapped genes, called focus genes, were overlaid onto a global molecular network using information supplied by the IPA knowledge base.

With the same approach described above, we have constructed a molecular network using datasets describing the short-term response to atrophy induction.

A cutoff value of 2.00 was implemented to identify genes whose expression was significantly differentially regulated, overlaying them onto a global molecular network using information from the IPKB.

Integrating data from different high-throughput measurement platforms (e.g. transcriptomic and proteomic) can be particularly useful to comprehensively detect disease-associated genes, and statistical approaches have been specifically developed to iteratively expand a molecular network using multiple data types (Hwang et al, 2005).

The network topology and parameter estimation challenge is very different in nature from other DREAM challenges, not only because it is the first one to address the dynamics of a bio-molecular network using a given biochemical (mechanistic) model, but also because it uses a credit system for participants to obtain in silico experimental data in an iterative manner.