In addition to providing the atomic coordinates, the predicted structures have further clarified some ambiguities that could not been uniquely determined by the existing experimental data, such as the directionality of the subunit arrangement in the heteropentamers.
The Release4 predictions were produced using GLEAN [9], an algorithm that creates consensus gene predictions by integrating available experimental data such as expressed sequence tags (ESTs) and proteomics data.
In conclusion, we developed a new generic tool, EuGene-P, to automatically and accurately annotate prokaryotic genomes by integrating genome-wide experimental data, such as RNA-Seq data.
Yet it is usually determined empirically, by using calibration procedures combining experimental data, such as load displacement or crack length data, with finite element simulation of fracture.
The present model is validated by the prediction of relevant experimental data such as strict graphitization degree (P1), average interlayer spacing (d002), electrical resistivity and thermal diffusivity for 51 graphitizable and semi-graphitizable carbons.
Empowered by a growing database of experimental data, such intelligent in-silico models may find applications in research and teaching.
In addition, only conclusive data, instead of experimental data such as gene expression data, have been utilized by Human Disease Network.
The theoretical assumptions are confirmed by experimental data obtained by several techniques, such as impedance spectroscopy, DSC, NMR and energy dispersive X-ray diffractometry.
In the recent past it was frequently assumed that newly detected viruses do behave genetically like their closest relatives without supporting such hypotheses by experimental data.
