To facilitate progress in these areas, we have established the Ribonucleotide Reductase database (RNRdb), a manually annotated and curated data source for annotation and comparative investigations centred on RNR biology.
For this reason, we decided to test: (i) the number of GIs detected by IslandViewer compared to those GIs detected in a few representative marine genomes that had been manually annotated and published and (ii) potential differences in functional annotation between the GIs detected by both approaches.
In our work we selected the probabilistic Stanford NLP tools, where the corpus data is gathered and manually annotated and then a model is trained to try to predict annotations depended on words and their contexts through weights.
Programs have been made to automatically compare the collected data with the existing data in the database and identify new data; the new data will be manually annotated; and the XML data files will be automatically updated to include the annotated new data.
From these gene sets, a subset of calls was manually annotated and a non-redundant extract of all calls together with additional EST sequences and controls were submitted for GeneChip design.
For each sequence, a keyframe is manually annotated, and in the remaining frames annotated landmarks are automatically aligned to face by the gradient descent AAM fitting algorithm in [115].
The target events are manually annotated and consist of 'Assembling a shelter,' 'Batting a run in,' and 'Making a cake.' The positive clips of each event amount to about 50 for training and 50 for testing.
Through the examination of both manually annotated and automatically extracted chemical mentions, it should be possible to better understand the chemical vocabulary and define the chemical space of published articles.
Our approach to stance classification of tweets is based on supervised machine learning, where a sample of tweets is first manually annotated and then used to train and evaluate a classifier.
In the first experiment, we used the ASOD [34] dataset comprised of input frames, a background frame, and the corresponding ground truth (manually annotated and automatically generated inaccurate masks) of static foreground from PETS2006 [35] , PETS2007 [36] , AVSS2007 [27]CVSG [37], VISOR [38], CANDELA [39], and WCAM [40].
All the assembled unigenes were searched against the following 7 database, including Nr (NCBI non-redundant protein sequences), Nt (NCBI non-redundant nucleotide sequences), Pfam (Protein family), KOG/COG (Clusters of Orthologous Groups of proteins), Swiss-Prot (A manually annotated and reviewed protein sequence database), KO (KEGG Ortholog database) and GO (Gene Ontology).
