Sentence examples for multiple protein datasets from inspiring English sources

Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences.

Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.

The first method presented here, the multiCleverMachine (multiCM), is an extension of the cleverMachine approach (CM [ 3]) to classify multiple protein datasets using physico-chemical properties.

In this work, we introduced two innovative approaches to compare multiple protein datasets using physico-chemical properties and GO annotations: the multiCM allows feature classification and the clever GO provides clustering through semantic relationships.

Here, we compared all the dataset against multiple protein databases as described earlier (Dixit et al., 2009).

The multiple sequence alignment for a given protein family is usually different in the various standard collections of protein families and the disagreement between protein datasets demonstrates that all multiple sequence alignment methods produce errors.

First, signal intensities and enrichment scores for GSTFs that have been measured in vitro on multiple protein binding microarrays were averaged within each of the two datasets.

Multiple protein sequence alignments (MSAs) were performed on both the full length regions and the conserved Pfam domains for the two datasets.

All sequences in E2 protein dataset were aligned using MAFFT multiple alignment server [ 26].

Multiple probes processing was also performed on the protein dataset and a 157×60 matrix (94 Entrez Gene IDs) was finally obtained.

Since multiple proteins often occur within a sentence (∼40% in the dataset), a small window of context would cause less ambiguity.