As more human disease phenotype data become available, our approach can be further improved by integrating new disease phenotype networks, given that the new networks contain different knowledge.
In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data.
By integrating this new data into our model, we predicted that mechanical stresses may activate the cAMP/PKA pathway in cardiac myocytes in a manner independent of β-adrenergic receptors.
Integrating new and existing data considerably improves the insight beyond analyzing either of the two datasets alone.
Together with capability to integrate new phylogeographic data, these methods make BiologicalNetworks a unique among other integration systems.
Given this ability, data never loses its value, and hence, it is always possible to extract new value from old data, by integrating it with new data.
The positions of the genes in the barley genome have been determined with high resolution by integrating the new PcG data with data that are publicly available.
By integrating all the new data in more and more sophisticated models, the major goals may emerge from the characterisation of new markers for diagnosis and prognosis and from the selection of pertinent and efficient new therapeutic targets.
The challenge, they noted, was to integrate new sources of data while protecting individual privacy.
The easy accessibility of these data, including processed files, has resulted into many researchers world-wide using the ENCODE resource to learn new biology, either by integrating existing data [1] or by integrating with their own new data [2].
