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Items 1 to 10 of about 2513
1. Kwon D, Kim S, Shin SY, Chatr-aryamontri A, Wilbur WJ: Assisting manual literature curation for protein-protein interactions using BioQRator. Database (Oxford); 2014;2014
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Assisting manual literature curation for protein-protein interactions using BioQRator.
  • The time-consuming nature of manual curation and the rapid growth of biomedical literature severely limit the number of articles that database curators can scrutinize and annotate.
  • Hence, semi-automatic tools can be a valid support to increase annotation throughput.
  • Although a handful of curation assistant tools are already available, to date, little has been done to formally evaluate their benefit to biocuration.
  • Moreover, most curation tools are designed for specific problems.
  • Thus, it is not easy to apply an annotation tool for multiple tasks.
  • BioQRator is a publicly available web-based tool for annotating biomedical literature.
  • It was designed to support general tasks, i.e. any task annotating entities and relationships.
  • In the BioCreative IV edition, BioQRator was tailored for protein- protein interaction (PPI) annotation by migrating information from PIE the search.
  • The results obtained from six curators showed that the precision on the top 10 documents doubled with PIE the search compared with PubMed search results.
  • It was also observed that the annotation time for a full PPI annotation task decreased for a beginner-intermediate level annotator.
  • This finding is encouraging because text-mining techniques were not directly involved in the full annotation task and BioQRator can be easily integrated with any text-mining resources.
  • Database URL: http://www.bioqrator.org/.

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  • [Copyright] © The Author(s) 2014. Published by Oxford University Press.
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  • (PMID = 25052701.001).
  • [ISSN] 1758-0463
  • [Journal-full-title] Database : the journal of biological databases and curation
  • [ISO-abbreviation] Database (Oxford)
  • [Language] ENG
  • [Grant] United States / Intramural NIH HHS / /
  • [Publication-type] Journal Article; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC4105708
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2. Lu Z, Wilbur WJ, McEntyre JR, Iskhakov A, Szilagyi L: Finding query suggestions for PubMed. AMIA Annu Symp Proc; 2009 Nov 14;2009:396-400
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Finding query suggestions for PubMed.
  • It is common for PubMed users to repeatedly modify their queries (search terms) before retrieving documents relevant to their information needs.
  • To assist users in reformulating their queries, we report the implementation and usage analysis of a new component in PubMed called Related Queries, which automatically produces query suggestions in response to the original user's input.
  • The proposed method is based on query log analysis and focuses on finding popular queries that contain the initial user search term with a goal of helping users describe their information needs in a more precise manner.
  • This work has been integrated into PubMed since January 2009.
  • Automatic assessment using clickthrough data show that each day, the new feature is used consistently between 6% and 10% of the time when it is shown, suggesting that it has quickly become a popular new feature in PubMed.

  • The Lens. Cited by Patents in .
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  • [Cites] J Am Med Inform Assoc. 2007 Mar-Apr;14(2):212-20 [17213501.001]
  • (PMID = 20351887.001).
  • [ISSN] 1942-597X
  • [Journal-full-title] AMIA ... Annual Symposium proceedings. AMIA Symposium
  • [ISO-abbreviation] AMIA Annu Symp Proc
  • [Language] ENG
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC2815412
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3. Kilaru P, Kim W, Sequeira W: Cocaine and scleroderma: is there an association? J Rheumatol; 1991 Nov;18(11):1753-5
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Cocaine and scleroderma: is there an association?
  • Scleroderma is a multisystem disease of unknown etiology.
  • A disease predominantly of women, it is distinctly rare in young men.
  • A case of scleroderma in a young male cocaine user is presented, and the possibility that cocaine may play a part in its etiology is explored.
  • [MeSH-major] Cocaine. Scleroderma, Systemic / chemically induced. Substance-Related Disorders / complications
  • [MeSH-minor] Adult. Humans. Male. Recurrence


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4. Kim S, Wilbur WJ: Thematic clustering of text documents using an EM-based approach. J Biomed Semantics; 2012 Oct 5;3 Suppl 3:S6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Thematic clustering of text documents using an EM-based approach.
  • Clustering textual contents is an important step in mining useful information on the web or other text-based resources.
  • The common task in text clustering is to handle text in a multi-dimensional space, and to partition documents into groups, where each group contains documents that are similar to each other.
  • However, this strategy lacks a comprehensive view for humans in general since it cannot explain the main subject of each cluster.
  • Utilizing semantic information can solve this problem, but it needs a well-defined ontology or pre-labeled gold standard set.
  • In this paper, we present a thematic clustering algorithm for text documents.
  • Given text, subject terms are extracted and used for clustering documents in a probabilistic framework.
  • An EM approach is used to ensure documents are assigned to correct subjects, hence it converges to a locally optimal solution.
  • The proposed method is distinctive because its results are sufficiently explanatory for human understanding as well as efficient for clustering performance.
  • The experimental results show that the proposed method provides a competitive performance compared to other state-of-the-art approaches.
  • We also show that the extracted themes from the MEDLINE® dataset represent the subjects of clusters reasonably well.

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  • (PMID = 23046528.001).
  • [ISSN] 2041-1480
  • [Journal-full-title] Journal of biomedical semantics
  • [ISO-abbreviation] J Biomed Semantics
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3465205
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5. Yeganova L, Comeau DC, Wilbur WJ: Machine learning with naturally labeled data for identifying abbreviation definitions. BMC Bioinformatics; 2011 Jun 09;12 Suppl 3:S6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Machine learning with naturally labeled data for identifying abbreviation definitions.
  • BACKGROUND: The rapid growth of biomedical literature requires accurate text analysis and text processing tools.
  • Detecting abbreviations and identifying their definitions is an important component of such tools.
  • Most existing approaches for the abbreviation definition identification task employ rule-based methods.
  • While achieving high precision, rule-based methods are limited to the rules defined and fail to capture many uncommon definition patterns.
  • Supervised learning techniques, which offer more flexibility in detecting abbreviation definitions, have also been applied to the problem.
  • However, they require manually labeled training data.
  • METHODS: In this work, we develop a machine learning algorithm for abbreviation definition identification in text which makes use of what we term naturally labeled data.
  • Positive training examples are naturally occurring potential abbreviation-definition pairs in text.
  • Negative training examples are generated by randomly mixing potential abbreviations with unrelated potential definitions.
  • The machine learner is trained to distinguish between these two sets of examples.
  • Then, the learned feature weights are used to identify the abbreviation full form.
  • This approach does not require manually labeled training data.
  • RESULTS: We evaluate the performance of our algorithm on the Ab3P, BIOADI and Medstract corpora.
  • Our system demonstrated results that compare favourably to the existing Ab3P and BIOADI systems.
  • We achieve an F-measure of 91.36% on Ab3P corpus, and an F-measure of 87.13% on BIOADI corpus which are superior to the results reported by Ab3P and BIOADI systems.
  • Moreover, we outperform these systems in terms of recall, which is one of our goals.

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  • (PMID = 21658293.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] ENG
  • [Grant] United States / Intramural NIH HHS / /
  • [Publication-type] Journal Article; Research Support, N.I.H., Intramural
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3111592
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6. Lee HJ, Park HS, Kim W, Yoon D, Seo S: Comparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach. Int J Genomics; 2014;2014:679437
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Comparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach.
  • The interrelationship between muscle and adipose tissues plays a major role in determining the quality of carcass traits.
  • The objective of this study was to compare metabolic differences between muscle and intramuscular adipose (IMA) tissues in the longissimus dorsi (LD) of Hanwoo (Bos taurus coreanae) using the RNA-seq technology and a systems biology approach.
  • The LD sections between the 6th and 7th ribs were removed from nine (each of three cows, steers, and bulls) Hanwoo beef cattle (carcass weight of 430.2 ± 40.66 kg) immediately after slaughter.
  • The total mRNA from muscle, IMA, and subcutaneous adipose and omental adipose tissues were isolated and sequenced.
  • The reads that passed quality control were mapped onto the bovine reference genome (build bosTau6), and differentially expressed genes across tissues were identified.
  • The KEGG pathway enrichment tests revealed the opposite direction of metabolic regulation between muscle and IMA.
  • Metabolic gene network analysis clearly indicated that oxidative metabolism was upregulated in muscle and downregulated in IMA.
  • Interestingly, pathways for regulating cell adhesion, structure, and integrity and chemokine signaling pathway were upregulated in IMA and downregulated in muscle.
  • It is thus inferred that IMA may play an important role in the regulation of development and structure of the LD tissues and muscle/adipose communication.

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  • (PMID = 25478565.001).
  • [ISSN] 2314-436X
  • [Journal-full-title] International journal of genomics
  • [ISO-abbreviation] Int J Genomics
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] Egypt
  • [Other-IDs] NLM/ PMC4247929
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7. Yoon KH, Lee SY, Kim W, Park JS, Kim HJ: Simultaneous determination of amoxicillin and clavulanic acid in human plasma by HPLC-ESI mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci; 2004 Dec 25;813(1-2):121-7
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Simultaneous determination of amoxicillin and clavulanic acid in human plasma by HPLC-ESI mass spectrometry.
  • A simple, fast and sensitive high-performance liquid chromatography (HPLC)-mass spectrometric (MS) method has been developed for simultaneous determination of amoxicillin and clavulanic acid in human plasma using terbutaline as internal standard.
  • After precipitation of the plasma proteins with acetonitrile, the analytes were separated on a C(8) reversed-phase column with formic acid-water-acetonirile (2:1000:100) and detected using electrospray ionization (ESI) mass spectrometry in negative selected ion monitoring (SIM) mode.
  • The method was validated and successfully applied to analysis of amoxicillin and clavulanic acid in clinical studies.
  • The limit of quantitation, 0.12 microg/ml for amoxicillin and 0.062 microg/ml for clavulanic acid, was five times lower than that of the published HPLC-UV method.
  • [MeSH-major] Amoxicillin / blood. Chromatography, High Pressure Liquid / methods. Clavulanic Acid / blood. Spectrometry, Mass, Electrospray Ionization / methods
  • [MeSH-minor] Calibration. Humans. Reference Standards. Reproducibility of Results. Sensitivity and Specificity

  • Hazardous Substances Data Bank. AMOXICILLIN .
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  • (PMID = 15556524.001).
  • [ISSN] 1570-0232
  • [Journal-full-title] Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
  • [ISO-abbreviation] J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Chemical-registry-number] 23521W1S24 / Clavulanic Acid; 804826J2HU / Amoxicillin
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8. Wilbur WJ, Rzhetsky A, Shatkay H: New directions in biomedical text annotation: definitions, guidelines and corpus construction. BMC Bioinformatics; 2006 Jul 25;7:356
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] New directions in biomedical text annotation: definitions, guidelines and corpus construction.
  • BACKGROUND: While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve.
  • We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize text that satisfies various types of information needs.
  • We report here the results of our inquiry into properties of scientific text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining of text for factual information.
  • Our ultimate goal is to annotate a significant corpus of biomedical text and train machine learning methods to automatically categorize such text along certain dimensions that we have defined.
  • RESULTS: We have identified five qualitative dimensions that we believe characterize a broad range of scientific sentences, and are therefore useful for supporting a general approach to text-mining: focus, polarity, certainty, evidence, and directionality.
  • We define these dimensions and describe the guidelines we have developed for annotating text with regard to them.
  • To examine the effectiveness of the guidelines, twelve annotators independently annotated the same set of 101 sentences that were randomly selected from current biomedical periodicals.
  • Analysis of these annotations shows 70-80% inter-annotator agreement, suggesting that our guidelines indeed present a well-defined, executable and reproducible task.
  • CONCLUSION: We present our guidelines defining a text annotation task, along with annotation results from multiple independently produced annotations, demonstrating the feasibility of the task.
  • The annotation of a very large corpus of documents along these guidelines is currently ongoing.
  • These annotations form the basis for the categorization of text along multiple dimensions, to support viable text mining for experimental results, methodology statements, and other forms of information.
  • We are currently developing machine learning methods, to be trained and tested on the annotated corpus, that would allow for the automatic categorization of biomedical text along the general dimensions that we have presented.
  • The guidelines in full detail, along with annotated examples, are publicly available.

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  • (PMID = 16867190.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] ENG
  • [Grant] United States / Intramural NIH HHS / /
  • [Publication-type] Journal Article; Research Support, N.I.H., Intramural; Research Support, U.S. Gov't, Non-P.H.S.
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC1559725
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9. Kim BM, Kim H, Kim W, Im KY, Park JK: Asymmetric protonation of ketone enolates using chiral beta-hydroxyethers: acidity-tuned enantioselectivity. J Org Chem; 2004 Jul 23;69(15):5104-7
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Asymmetric protonation of ketone enolates using chiral beta-hydroxyethers: acidity-tuned enantioselectivity.
  • New chiral hydroxyethers 1a-f were prepared for asymmetric protonation of achiral enolates prepared from prochiral ketones.
  • The enantioselectivity of protonation was highly dependent upon the acidity of the chiral alcohols, the highest enantioselectivity (90% ee) being achieved with 3,5-dichloro-substituted beta-hydroxyether 1c.
  • A salt-free enolate generated from trimethylsilyl enol ether 4 provided product of the highest ee.
  • Unlike other reagents, chloro-substituted alcohols provided almost consistent enantioselections throughout the reaction temperatures examined (-25 to -98 degrees C).
  • Protonation of other aromatic ketones showed selectivity similar to that of 2-methyl-1-tetralone.

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  • (PMID = 15255744.001).
  • [ISSN] 0022-3263
  • [Journal-full-title] The Journal of organic chemistry
  • [ISO-abbreviation] J. Org. Chem.
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] United States
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10. Smith L, Yeganova L, Wilbur WJ: Hidden Markov models and optimized sequence alignments. Comput Biol Chem; 2003 Feb;27(1):77-84
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Hidden Markov models and optimized sequence alignments.
  • We present a formulation of the Needleman-Wunsch type algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a hidden Markov process.
  • The fully trainable model is applied to two problems in bioinformatics: the recognition of related gene/protein names and the alignment and scoring of homologous proteins.
  • [MeSH-major] Computational Biology. Markov Chains. Models, Statistical. Sequence Alignment / methods. Sequence Alignment / statistics & numerical data
  • [MeSH-minor] Animals. Databases, Genetic. Genes / genetics. Humans. MEDLINE / statistics & numerical data. Mice. Sequence Homology, Nucleic Acid

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  • (PMID = 12798042.001).
  • [ISSN] 1476-9271
  • [Journal-full-title] Computational biology and chemistry
  • [ISO-abbreviation] Comput Biol Chem
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] England
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