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A different approach is to use massively parallelized methods on large supercomputers to improve computation time.
Due these reasons, the practical applicability and robustness of such methods on large sample high dimensional datasets are questionable.
Most importantly, PS-IRV has been confirmed to outperform the other methods on large sets at every cutoff.
The third stream mainly studied financial networks from a technical perspective by adopting various data mining methods on large social media datasets.
When examining these datasets, and then turning to applying our methods on large blocks of genomic data, there seems to be a "contrast" problem in the recognition of the start-of-coding region when working with the standard 1st order HMM (a "needle-in-the-haystack" problem).
From CompStat data collection used to allocate police resources to predictive models used to assist a parole board's decision to release an inmate to probation supervision programs, the use of statistical methods on large datasets of criminal offending rates to estimate criminal justice outcomes are everywhere.
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To address this limitation, the authors plan to validate their methods on larger databases, including normal recordings, obtained from different data acquisition systems.
Although enzymatic systems and whole-cell biocatalysts have been developed for the synthesis of Neu5Ac, low yield and productivity still hamper the use of these methods on larger scales.
The acceleration capabilities offered by modern processors (many-core and multicore) enables performing all-to-all PSC using different methods on large-scale datasets and compare results.
Nonetheless, such approaches do not capture a true tissue-specific GRN, which, if available, would probably further improve the accuracy of supervised methods on large-scale data.
Our work reinvestigates this conclusion in the context of improved document retrieval methods on larger document and query collections.
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