We used the silkworm CDS database to identify and to remove sequences from degradations of mRNAs, and we also confirmed the candidate sequences by identifying query sequences among our predicted miRNAs and in miRBase 10.0.
Because of the stringent alignment threshold used for identifying query and template domain instances, lots of instances with large sequence variation under the same domain ID are filtered out, leaving very few domain instances for study.
This framework allows PLASTIC, unlike the state-of-the-art in commercial optimizers, to identify query similarity at a much broader level, including handling changes in projection, selection, and join predicates, as also in the query tables themselves.
We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that exploits "evidence". We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries form Yahoo!, and using a variety of metrics.
We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that exploits "evidence". We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries from Yahoo!, and using a variety of metrics.
Alignment results for the Newbler isotigs and MIRA3 contigs from the BLASTx phase of the BLAST2GO annotation (see below) process were queried to identify query start, query end, and SNP position.
Each finished genome sequence was analysed with ISfinder [ 1] to identify query ISs for testing as described above, and nucleotide BLAST was used to confirm the precise locations and orientations of each IS in each genome.
In a study published in early June, Horvitz and his colleagues identified queries that provided strong evidence of a recent diagnosis of pancreatic cancer.
Given a query q, we first consider Simrank as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in.
Twenty-eight terms were used to identify queries for both still and moving images, resulting in a subset of 33,149 image queries by 9855 users.
The theory also supports item analysis, which identifies queries that are hampering the test's reliability, and which may be candidates for refinement or removal.
