Sentence examples for datasets from different biological from inspiring English sources

We have evaluated these biclustering algorithms on six real datasets from different biological domains: yeast to plant to different cancers.

High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data.

To generate a resource of gene function predictions for Drosophila melanogaster, we applied an approach which used biological relationships to train individual classifiers for each specific functional category (here either a GO term or a KEGG pathway), thereby optimizing the importance of each feature extracted from different biological datasets for the prediction of each function.

Additionally, this method could be readily applied to metabolic profiling data from different biological systems or to other profiling datasets such as those acquired in proteomics and transcriptomics.

Thus, with our analysis, we were able to achieve comparability of microarray datasets from different laboratories addressing different biological questions.

Here we present ChIP-seq datasets from different antibodies and biological replicates to show data variability from raw sequencing reads to the number of MACS1.4 peaks.

Finally, a powerful feature of TRAM is its ability to compare, within the same analysis, the transcriptome maps derived from two datasets (or two pools of datasets) related to different biological conditions (indicated as 'A' and 'B'), such as two tissues or cell types, two developmental stages, normal vs pathological cells or cells maintained in absence or in presence of a substance.

It can not only provide an additional tool for further annotation of vector genome, but can also serve as a novel platform to integrate datasets from different experiments in order to dissect complicated biological processes in insect vectors.

Motivation: With the availability of many 'omics' data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels.

Artificial intelligence and information integration – the ability to easily merge large, messy datasets from different sources – are set to take things far further than that.

Artificial intelligence and information integration the ability to easily merge large, messy datasets from different sources are set to take things much further than that.