Sentence examples for using experimental datasets from inspiring English sources

The results are validated using experimental datasets.

We performed a functional analysis of the subtype signatures using Signaling Pathway Enrichment using Experimental Datasets (SPEED) [ 45], and enrichment analyses on the Molecular Signatures Database (MSigDB) [ 46], and KEGG [ 39] and Pathway Interaction Database (PID) [ 47] using BioMyn [ 48].

To further characterize the CRC subtypes at a functional level, we subjected the lists of subtype signature genes to a functional analysis using Signaling Pathway Enrichment using Experimental Datasets (SPEED) [ 45], the Molecular Signatures Database (MSigDB) [ 46], and KEGG [ 39] and Pathway Interaction Database (PID) [ 47] using BioMyn [ 48].

Typically researchers use experimental datasets such as [ 13] constructing the various atrial regions (pectinate muscle, crista terminalis, Bachmann's bundle, etc)., each of which has different electrical properties.

We used experimental datasets of comprehensive genome wide knock-out screens of Escherichia coli [ 17, 18] and Pseudomonas aeruginosa [ 19, 20] to train the machines with a large variety of attributes including topology characteristics as mentioned above, own developments on evaluating possible flux deviations [ 6], and genomic and transcriptomic information.

In summary, the incorporation of this complete dataset in miRGate has improved the prediction reach of the individual methods (a 10 21% improvement in performance), as seen by the comparison of the whole set versus individual methods when using experimental confirmed datasets.

Table 1 The number of significant transformations identified using experimental and predicted datasets Endpoint Significant transformations Experimental-based Prediction-based EINECS ChemDiv Aquatic toxicity 119 1552 5301 Ames test 132 442 4397 Clearly, prediction-based analysis provides significantly more transformations.

We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD).

In this study, regular and irregular wave runups were investigated based on scaling arguments using available experimental datasets.

The prediction models were constructed using 768 experimental datasets from the literature with 8 input parameters and 2 output parameters (cooling load (CL) and heating load (HL)).

Ensemble modeling (EM) was developed to circumnavigate this challenge and effectively sample the large kinetic parameter solution space using consistent experimental datasets.