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Section 3 presents parallel platforms for Big Data elaboration, while Section 4 is concerned with the approaches for data annotation, specifically considering the methods employed in the computational biology field.
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SESL reused existing controlled vocabularies only for data annotation.
Together, these different approaches provide complementary views of the dataset that can now be integrated in a comprehensive, fully probabilistic pipeline for metabolome data annotation.
Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions.
As such, there is a great need for further experimental validation or complementary annotation approaches for conventional genome annotation.
In addition, data annotation for natural language understanding and speech recognition is quite burdensome.
For Affymetrix data, annotations packages from Bioconductor were used.
In this study, we employed both categorical and dimensional approaches for emotion annotation.
Novel approaches for structural annotation of genomes are required.
Software solutions for performing peak detection, retention-time alignment, data annotation, and statistics have greatly increased the feasibility of applying the untargeted metabolomic approach to biological problems.
GEO analysis has become a commonly utilized approach for functional annotation of large-scale genomic data.
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