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The results given by the reduced database model are compared with full-scale results.
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We used the human and mouse datasets to test the reduced database workflow by creating reduced databases comprised of only those proteins with transcript abundances above 1 TPM.
The Perl Cache::Memcached::Fast library can reduce database load by caching and preloading frequently used data in memory.
This server-side caching aims to reduce database requests by feeding the production of HTTP header fields used for HTTP client caching as described below.
The reduced KEGG database was generated by removing entries for which no KEGG orthology (KO) assignments existed and subsequently clustering each KO individually (uclust v1.5.579, using 85% sequence identity as the clustering cutoff) (Edgar, 2010; Kanehisa and Goto, 2000; Kanehisa et al., 2014).
Therefore, most data accessed by public users already reside in memory, which reduces database access and results in faster data delivery to the users.
This enables us to effectively discover multi-temporal patterns in large-scale temporal databases by reducing the database scan in the generation of candidate patterns.
This data pruning reduced the database size or the model size by about 30%, and consequently saved the computation time and memory usage in speech recognition.
These steps reduced the database to 477 current entries.
Offsetting its need for a homogeneous QTL dataset, meta-QTL analysis allows an advance over browsing the database by reducing the number of observed QTLs to a more limited number of meta-QTLs with a narrower confidence interval.
The tag search time in the database is reduced by using the hash value as the address of the corresponding tag.
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