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For 4601 transcripts with reliable annotation (see Materials), the gene expression measurement according to the probes in the annotated direction was highly correlated to the number of reads produced by the sequencing.
Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models.
It was also essential in producing a reliable annotation for human and mouse MPSS signatures[6].
However, we only considered data from the U133A human array since we had reliable annotation data only for this chip.
However, we cannot depend on hand curation and wet-lab experiments for accurate, reliable annotation as the rate of sequencing continues to accelerate.
Therefore, reliable annotation and/or identification of the detected metabolites is essential.
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Following this analysis we created a collection of human-mouse conserved sequence tags and carefully compared our results to reliable annotations in order to benchmark the reliability of our classifications.
Besides, reliable annotations were found for 48 sequences out of 58 that were not previously annotated in the stickleback transcriptome.
This high-throughput technique will increase the filling-in of specialized databases such as Norine with external data and will help to add new and more reliable annotations to the original databases.
However, this approach is not optimal for Xenopus tropicalis, as only a limited number of reliable annotations are available.
Combined with plurality voting [76], selecting reciprocal matches stringently removes false positives and increases specificity so as to yield reliable annotations in positive, negative, experimental, and large scale controls that improve on existing template methods [69].
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