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Using spatial curves and popular categorical features, such as target site length and location, we train a linear SVM model, utilizing experimental CLIP-seq data.
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Our understanding of cancer has evolved mainly from results of studies utilizing experimental models.
Our overall findings are in agreement with previous studies utilizing experimental cellular models of bacterial internalization, using either intracellular or facultative intracellular pathogens.
Estimation of kinetic parameters is an important and difficult step in the development of models, but calculations of unknown kinetic parameters can be achieved by utilizing experimental data and model-based technique.
In this paper, we adapt the model to utilize experimental data by including information on different drug concentrations.
The model will utilize experimental data on the types of mutants that arise in the context of different treatments.
As discussed above, utilizing experimental data is essential to extract relevant biological conclusions regarding the particular scenario being modeled.
Since this time, many other interspecies crosses have been described that produce animals displaying genetic predisposition to various types of induced tumors (i.e., require treatment of backcross hybrids to develop melanoma), and these are still actively utilized experimental models for assessment of genetic interactions leading to tumor development [ 6, 7].
To corroborate these results and demonstrate similar findings in human derived cells, our second experimental model utilized the Caco-2 colon cancer line.
Our experimental model utilized both IFN-γ and LPS to fully activate STAT1.
Our experimental model utilized an extended duration of exposure of the muscle to antioxidants at 37°C compared with most other studies.
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