Sentence examples for calculating the semantic from inspiring English sources

mspecLINE associates diseases with proteins by calculating the semantic distance between annotated terms from a controlled biomedical vocabulary.

mspecLINE associates diseases with proteins by calculating the semantic distance between MeSH annotations in MEDLINE using Normalized Google Distance (NGD).

We use the most informative common ancestor method of calculating the semantic similarity between two proteins, as outlined in [ 27].

The kNN algorithm was then used to calculate the semantic similarity between two string vectors.

In particular, we build a fuzzy ontology to model uncertain relationships between objects in databases for service matching, and present a novel analytic hierarchy process approach to calculate the semantic similarity between concepts.

First, we use the most informative common ancestor method to calculate the semantic similarity between two GO terms [ 15].

For PPI predictions, the modules obtained were assigned using NetworkBLAST and then calculated the semantic similarity between the GO terms included and the GO terms predicted.

With this data source, we calculate the semantic similarity between any pair of the 5,549 genes using the tool GOSemSim [ 35, 36].

The resulting linked data-based TF-IDF vectors are then combined with the cosine similarity measure to calculate the semantic similarity between two RDF resources.

mspecLINE builds sets of MeSH disease descriptors and MeSH protein descriptors, then calculates the semantic distances between each pair of descriptors using NMD.

To calculate the semantic similarity for a pair of genes, the minimal p(c) of parental nodes shared by two genes will be identified.