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META is a new knowledge-based expert system that provides computer simulation of the biotransformation of chemicals. The program is based on the recognition of key functional groups within the complete chemical structure and therefore can predict the metabolites of new xenobiotics. Here, we describe a comprehensive knowledge base built for the purposes of modeling mammalian metabolism with META methodology.
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BACKGROUND: Understanding research activity within any given biomedical field is important. Search outputs generated by MEDLINE/PubMed are not well classified and require lengthy manual citation analysis. Automation of citation analytics can be very useful and timesaving for both novices and experts. RESULTS: PubFocus web server automates analysis of MEDLINE/PubMed search queries by enriching them with two widely used human factor-based bibliometric indicators of publication quality: journal impact factor and volume of forward references. In addition to providing basic volumetric statistics, PubFocus also prioritizes citations and evaluates authors' impact on the field of search. PubFocus also analyses presence and occurrence of biomedical key terms within citations by utilizing controlled vocabularies. CONCLUSION: We have developed citations' prioritisation algorithm based on journal impact factor, forward referencing volume, referencing dynamics, and author's contribution level. It can be applied either to the primary set of PubMed search results or to the subsets of these results identified through key terms from controlled biomedical vocabularies and ontologies. NCI (National Cancer Institute) thesaurus and MGD (Mouse Genome Database) mammalian gene orthology have been implemented for key terms analytics. PubFocus provides a scalable platform for the integration of multiple available ontology databases. PubFocus analytics can be adapted for input sources of biomedical citations other than PubMed.
OBJECTIVE: Natural language processing (NLP) approaches have been explored to manage and mine information recorded in biological literature. A critical step for biological literature mining is biological named entity tagging (BNET) that identifies names mentioned in text and normalizes them with entries in biological databases. The aim of this study was to provide quantitative assessment of the complexity of BNET on protein entities through BioThesaurus, a thesaurus of gene/protein names for UniProt knowledgebase (UniProtKB) entries that was acquired using online resources. METHODS: We evaluated the complexity through several perspectives: ambiguity (i.e., the number of genes/proteins represented by one name), synonymy (i.e., the number of names associated with the same gene/protein), and coverage (i.e., the percentage of gene/protein names in text included in the thesaurus). We also normalized names in BioThesaurus and measures were obtained twice, once before normalization and once after. RESULTS: The current version of BioThesaurus has over 2.6 million names or 2.1 million normalized names covering more than 1.8 million UniProtKB entries. The average synonymy is 3.53 (2.86 after normalization), ambiguity is 2.31 before normalization and 2.32 after, while the coverage is 94.0% based on the BioCreAtive data set comprising MEDLINE abstracts containing genes/proteins. CONCLUSION: The study indicated that names for genes/proteins are highly ambiguous and there are usually multiple names for the same gene or protein. It also demonstrated that most gene/protein names appearing in text can be found in BioThesaurus.
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Dr. Genryo Torafumi Okuyama, the second son of Dr. Genchu Okuyama of the Kaminoyama clan, was born on Dec. 4th, 1847. His elder brother Dr. Toraakira Okuyama was promoted to Dai Ikan (Senior Captain), the highest rank of medical officer in the Japanese Navy, and rendered distinguished services in the establishment of the naval medical systematization in the early Meiji era. Dr. Trafumi Okuyama, who was appointed as medical officer of the Yokohama army Hospital and transferred to Daibyoin in Edo, was engaged in medical treatment of injured soldiers during the Boshin-war in 1868. He went to Kagoshima with William Willis and as one of the founders of the Kagoshima Medical school, gave students education there. He resigned his naval position in 1874, when he was Dai Gun I (Senior Leutenant) and died at the age of 41 in April 16th 1887. Dr. Torafumi Okuyama compiled A medical vocabulary in English and Japanese ("Igo Ruizyu") and Deutsch-Japanisches Hand-Wörterbuch für Medizin ("Dokuwa Igaku Ziten) and published "Koen Hikki", the translation of the lectures by Dr. Edwin Wheeler.
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OBJECTIVES: To develop an automated, accurate and scalable method by which acronym-definition pairs can be identified within text. Its primary advantage is in enabling information processing methods to resolve author-defined acronyms, but it also allows an automated creation of a reference work on acronym definitions. This has several advantages over manual or semi-automated methods, besides time and effort saved, such as enabling identification of relative frequencies for alternate acronyms and definitions as well as spelling, phrasing and hyphenation variants for a unique acronym-definition pair. It also aids users in identifying acronym/definition variants present in the literature that may not necessarily be in biomedical databases. METHODS: A set of heuristics to accurately locate and identify the boundaries of acronym-definition pairs was developed and refined in terms of precision and recall on subsets of MEDLINE records. These training sets were gradually increased in size and heuristics re-evaluated to ensure scalability. RESULTS: Our final set of Acronym Resolving General Heuristics (ARGH) had a sample-based estimated rate of 96.5 +/- 0.4% precision and 93.0 +/- 2.7% recall when tested on over 12 million MEDLINE records, identifying more than 174,000 unique acronyms and their 737,000 associated definitions. CONCLUSIONS: We estimate that as much as 36% of the acronyms in MEDLINE are associated with more than one definition and, conversely, up to 10% of definitions are associated with more than one acronym. The number of unique acronyms in MEDLINE is increasing at a rate of approximately 11,000 per year, while the number of definitions associated with them is growing at approximately four times that rate. Access to the ARGH database is available online at http://lethargy.swmed.edu/ARGH/argh.asp. The heuristic module and database are available upon request.
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