Sentence examples for intuitive networks from inspiring English sources

inSARa (intuitive networks for Structure-Activity-Relationships analysis) was primarily developed with the objective to support the medicinal chemist in tackling SAR analysis and visualization of large data sets in a more intuitive way than fingerprint-based approaches [1].

To tackle these limitations our new method, inSARa (intuitive networks for Structure-Activity-Relationships analysis), combines MCS similarity with the reduced graph (RG) concept [3] for SAR network generation.

To circumvent this and other drawbacks (e.g. the exact match or incomplete ring problems) our own in-house strategy INSARA (intuitive networks for Structure-Activity-Relationships analysis) employs reduced graphs (RG) instead of the original molecules in order to reduce the complexity of the problem to a manageable size.

The examples shown thus far have dealt with intuitive networks arising from a priori quantitative phenomena (stock price correlations, airline traffic); however, LANS is also useful for discovering important structure in feature-based representations of objects/phenomena.

I'm not sure those are the most intuitive network names, but I suppose all that matters is that developers know how to incorporate the experience into their games and apps.

It used to be a streamlined and intuitive network; now it's messy, ugly, intrusive.

Clearly, this would prevent us from implementing an intuitive network-based visualization.

Cytoscape [ 36], a collaborative open-source project, is a widely used and intuitive network visualization and analysis tool in genomics research.

Hard thresholding using the signum function leads to intuitive network concepts (e.g., the node connectivity equals the number of direct neighbors), but it may lead to a loss of information.

In summary, FluxMap combines a simple, fast and source-independent data import of flux data with different parameters for generating user-specific visualizations as well as intuitive network exploration capabilities.

Hard thresholding using the signum function leads to intuitive network concepts (e.g., the node connectivity equals the number of direct neighbors), but it may lead to a loss of information: if τ has been set to 0.8, there will be no connection between two nodes if their similarity equals 0.79.