Among all other methods, only Chuang et al. (2007) leverages experimental data [6], however with an entirely different scope and method.
Accurate computational methods to infer gene regulatory networks, particularly methods that leverage genome-scale experimental data, are urgently required not only to supplement empirical approaches but also, if possible, to explore these data in new more-integrative ways.
These empirical equations have been mostly constructed in some ways by leveraging regression procedures on experimental data, usually laboratory observations (thus from small/medium scale experiments).
Experimental data were leveraged for sub-process model performance and integrated into an engineering system model of the process.
This must go well beyond a web portal to create an interactive platform integrating simulation, experimental data, and analytics, while leveraging semantic web technologies to support the federated storage of data across geographically dispersed sites.
With the advent of modern proteogenomic technologies and bioinformatic tools, it becomes increasingly feasible to integrate and visualize such complex types of data by leveraging existing network and pathway knowledge and experimental data, thereby enabling new biological hypothesis generation and experimental validation.
The Leverage statistical approach is herein applied for evaluation of experimental data of the paraffin waxes/diamondoids solubility in gaseous systems.
We hypothesize that uncertainty in the corresponding experimental data could be the cause; we prove this by applying a Leverage approach, while studying the validity and applicability domain of the model.
Finally, the Leverage algorithm, which is a novel statistical approach, is implemented to assess the quality of the experimental data and diagnose the probable doubtful data samples.
In addition, the Leverage approach was implemented to check the reliability of the proposed model and the quality of experimental data.
