Sentence examples for cellular function prediction from inspiring English sources

Deng et al. [ 7] have used an MRF model to integrate multiple sources of data including the protein complexes while Joshi et al. [ 32] have made use of protein complexes by assigned binary interactions to two proteins involved in a same protein complex and developed an integrated probabilistic method for cellular function prediction.

To determine the role that miR-203 can play in the influence of cellular function, putative target prediction was carried out and PKC- α was determined to be a target [ 61].

Network analysis of the expression data was used to predict the consequence of each transfection on cellular function and these predictions were experimentally tested.

The cellular role predictions "Cell envelope", "Regulatory functions", "Central intermediary metabolism" "Purines and pyrimidines" show significant enrichment.

These modules correspond to the functional units of the network, are composed of groups of highly connected proteins involved in the same cellular function28 and permit function prediction when containing uncharacterized proteins29,30.

Final annotation was performed using 1) BLASTP comparison to proteins in the NCBI nr database, 2) CDS identification, COG function prediction, cellular location prediction, and identification of Pfam domains and PROSITE motifs using the BASys Bacterial Annotation System [145] and 3) additional Pfam domain identification using the Sanger Centre Pfam search engine [146].

Brun, C. et al. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network.

These models currently are prominent as one of the most promising approaches to achieve an in silico prediction of cellular function in terms of physiology [ 17].

The prediction of cellular function and the GO category by ProtFun relies on a large number of other sequence derived protein features, including predicted post translational modifications (PTMs), protein sorting signals and physical/chemical properties, rather than relying on sequence similarity protein [ 97, 98].

The method has formerly led to the prediction of the cellular function of uncharacterized yeast proteins [ 22] and the definition of a scale of functional divergence for yeast paralogs based on PPIs [ 13].

AFP: Automated function prediction; BP: Biological process; CC: Cellular component; GO: Gene ontology; MF: Molecular function; RW: Random walk.