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1.  BLAST: a more efficient report with usability improvements 
Nucleic Acids Research  2013;41(Web Server issue):W29-W33.
The Basic Local Alignment Search Tool (BLAST) website at the National Center for Biotechnology (NCBI) is an important resource for searching and aligning sequences. A new BLAST report allows faster loading of alignments, adds navigation aids, allows easy downloading of subject sequences and reports and has improved usability. Here, we describe these improvements to the BLAST report, discuss design decisions, describe other improvements to the search page and database documentation and outline plans for future development. The NCBI BLAST URL is http://blast.ncbi.nlm.nih.gov.
doi:10.1093/nar/gkt282
PMCID: PMC3692093  PMID: 23609542
2.  Education resources of the National Center for Biotechnology Information 
Briefings in Bioinformatics  2010;11(6):563-569.
The National Center for Biotechnology Information (NCBI) hosts 39 literature and molecular biology databases containing almost half a billion records. As the complexity of these data and associated resources and tools continues to expand, so does the need for educational resources to help investigators, clinicians, information specialists and the general public make use of the wealth of public data available at the NCBI. This review describes the educational resources available at NCBI via the NCBI Education page (www.ncbi.nlm.nih.gov/Education/). These resources include materials designed for new users, such as About NCBI and the NCBI Guide, as well as documentation, Frequently Asked Questions (FAQs) and writings on the NCBI Bookshelf such as the NCBI Help Manual and the NCBI Handbook. NCBI also provides teaching materials such as tutorials, problem sets and educational tools such as the Amino Acid Explorer, PSSM Viewer and Ebot. NCBI also offers training programs including the Discovery Workshops, webinars and tutorials at conferences. To help users keep up-to-date, NCBI produces the online NCBI News and offers RSS feeds and mailing lists, along with a presence on Facebook, Twitter and YouTube.
doi:10.1093/bib/bbq022
PMCID: PMC2984538  PMID: 20570844
Bioinformatics; education; tutorials; NCBI; databases; GenBank
3.  SemCat: Semantically Categorized Entities for Genomics 
We describe the construction of a semantic database called SemCat consisting of a large number of semantically categorized names relevant to genomics. SemCat can be used to facilitate natural language processing in MEDLINE. We present suitable application areas including biomedical name classification and named entity recognition.
PMCID: PMC1839293  PMID: 17238442
4.  GENETAG: a tagged corpus for gene/protein named entity recognition 
BMC Bioinformatics  2005;6(Suppl 1):S3.
Background
Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition.
Results
To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching.
Conclusion
The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at . A newer version of GENETAG-05, will be released later this year.
doi:10.1186/1471-2105-6-S1-S3
PMCID: PMC1869017  PMID: 15960837

Results 1-5 (5)