For the VSM similarity, q j is a query j of the matrix representing a conjunction of entities, t is the number of biological entities of the network, w x, i is the weight of entity e x in document d i, w x, j is the weight of entity e x in query q j.
Many existing enrichment methods require selections of (arbitrary number of) the most significant entities and/or do not account for weights of entities.
Several authors (Goeman and Bühlmann, 2007; Gold et al., 2007; Huang et al., 2009) have raised the issue of correlation between weights of entities: generally the weights of biologically related genes or proteins change together and therefore a null model assuming independence between weights may result in exaggerated P-values.
Users are given the ability to search with any combination of text sources and ontologies and can adjust the weights of these entities as desired to customize retrieval of specific queries.
Next, three tables '//entity/type' (for filtering whether the entity is a polymer or not), '//entity/pdbx_number_of_molecules' (for counting the number of the entity in the entry), and '//entity/formula_weight' (for the molecular weight of the single entity) are joined with the same condition as in the previous query, that is, all being under the same 'entity' element.
where M subscript denotes molecular weight of the subscripted entity, and X wt subscript is the corresponding weight fraction (in g ZrO2/kg H2O) that was derived from the Raman spectra as discussed above.
The second part is extracting prominent name entity, which contains representation of word, computing weight of word, and name entity filtration.
And the weight of relations between homogeneous entities is set as β.
This part contains representation of word, computing weight of word, and name entity filtration.
(10 where ( beta ) is the weight of the relation between homogeneous entities.
