This paper addresses these drawbacks by proposing three efficient approaches for mining CARs in large datasets relying on parallel computing.
Many of these techniques to connect these large datasets rely on modern machine learning tools and have been developed as a part of Lee's doctoral thesis.
The characterization of metagenomic datasets relies on the use of reference databases that contain sequences of known origin and phenotype.
Phylogenetic classification of the metagenomic datasets relies on the use of the above-mentioned reference databases that contain sequences of known origin and gene function.
Older high throughput datasets rely on yeast two-hybrid (Y2H) studies, which can measure interactions between two proteins that would never even encounter each other in nature.
The emergence of new fields such as macroecology, dedicated to the analysis of large-scale multispecies datasets, relies on the availability of disparate sources of data in order to uncover broad patterns in ecological and evolutionary processes.
Traditional techniques that analyze gene expression datasets rely on clustering techniques that do not take advantage of pre-existing information about pathways thus not taking account of biological context.
It must be recognized that the high potential value of these datasets relies to a large extent on the quality of the biological and clinical annotations, which becomes significant only if the experimental data are properly collected and described.
Clustering the transcriptomic profile of 587 triple-negative breast cancer (TNBC) cases extracted from 21 breast cancer microarray datasets relying on the lack of transcript-level expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2), Lehmann and colleagues have categorized the TNBCs into seven subgroups [ 1].
The quality of the acquired dataset relies, however, on the expertise of researchers and unsupervised operations might result in low data quality.
