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1. Yu H, Kim T, Oh J, Ko I, Kim S, Han WS: Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS. BMC Bioinformatics; 2010;11 Suppl 2:S6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.
  • BACKGROUND: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results.
  • Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function.
  • However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy.
  • This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed.
  • RESULTS: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback.
  • RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time.
  • An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation.
  • Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process.
  • RefMed is accessible at http://dm.postech.ac.kr/refmed.
  • CONCLUSIONS: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback.
  • It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.
  • [MeSH-major] Algorithms. Artificial Intelligence. Computational Biology / methods. Database Management Systems. PubMed
  • [MeSH-minor] Data Interpretation, Statistical. Feedback. Reproducibility of Results. User-Computer Interface

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  • (PMID = 20406504.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3165966
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2. Lu Z, Kim W, Wilbur WJ: Evaluating relevance ranking strategies for MEDLINE retrieval. J Am Med Inform Assoc; 2009 Jan-Feb;16(1):32-6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Evaluating relevance ranking strategies for MEDLINE retrieval.
  • This paper evaluates the retrieval effectiveness of relevance ranking strategies on a collection of 55 queries and about 160,000 MEDLINE((R)) citations used in the 2006 and 2007 Text Retrieval Conference (TREC) Genomics Tracks.
  • The authors study two relevance ranking strategies: term frequency-inverse document frequency (TF-IDF) weighting and sentence-level co-occurrence, and examine their ability to rank retrieved MEDLINE documents given user queries.
  • Furthermore, the authors use the reverse chronological order-PubMed's default display option-as a baseline for comparison.
  • Retrieval effectiveness is assessed using both mean average precision and mean rank precision.
  • Experimental results show that retrievals based on the two strategies had improved performance over the baseline performance, and that TF-IDF weighting is more effective in retrieving relevant documents based on the comparison between the two strategies.

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  • (PMID = 18952932.001).
  • [ISSN] 1067-5027
  • [Journal-full-title] Journal of the American Medical Informatics Association : JAMIA
  • [ISO-abbreviation] J Am Med Inform Assoc
  • [Language] ENG
  • [Grant] United States / Intramural NIH HHS / /
  • [Publication-type] Evaluation Studies; Journal Article; Research Support, N.I.H., Intramural
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2605593
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3. Poulter GL, Rubin DL, Altman RB, Seoighe C: MScanner: a classifier for retrieving Medline citations. BMC Bioinformatics; 2008 Feb 19;9:108
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] MScanner: a classifier for retrieving Medline citations.
  • BACKGROUND: Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline.
  • However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance.
  • Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance.
  • A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains.
  • RESULTS: MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance.
  • For maximum speed it uses the Medical Subject Headings (MeSH) and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline.
  • The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline.
  • We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic.
  • In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92.
  • CONCLUSION: MScanner is an effective non-domain-specific classifier that operates on the entire Medline database, and is suited to retrieving topics for which many features may indicate relevance.
  • Its web interface simplifies the task of classifying Medline citations, compared to building a pre-filter and classifier specific to the topic.
  • The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material, and the web interface may be accessed at http://mscanner.stanford.edu.

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  • (PMID = 18284683.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] ENG
  • [Grant] United States / FIC NIH HHS / TW / D43 TW006993; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / FIC NIH HHS / TW / D43 TW06993; United States / NIGMS NIH HHS / GM / U01GM61374
  • [Publication-type] Comparative Study; Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2263023
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4. Lu Z: PubMed and beyond: a survey of web tools for searching biomedical literature. Database (Oxford); 2011;2011:baq036
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] PubMed and beyond: a survey of web tools for searching biomedical literature.
  • The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature.
  • This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters.
  • However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth.
  • In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement.
  • Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications.
  • These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed.
  • In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development.
  • Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search.
  • Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field.
  • Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/search.

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  • (PMID = 21245076.001).
  • [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; Review
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3025693
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5. Fontaine JF, Barbosa-Silva A, Schaefer M, Huska MR, Muro EM, Andrade-Navarro MA: MedlineRanker: flexible ranking of biomedical literature. Nucleic Acids Res; 2009 Jul;37(Web Server issue):W141-6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] MedlineRanker: flexible ranking of biomedical literature.
  • The biomedical literature is represented by millions of abstracts available in the Medline database.
  • These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine.
  • This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic.
  • Additionally, when searching for more general topics, the same approach may return hundreds of unranked references.
  • To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts.
  • We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge.
  • Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection.
  • These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance.
  • We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time.
  • MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker.
  • [MeSH-major] Information Storage and Retrieval / methods. MEDLINE. Software
  • [MeSH-minor] User-Computer Interface

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  • (PMID = 19429696.001).
  • [ISSN] 1362-4962
  • [Journal-full-title] Nucleic acids research
  • [ISO-abbreviation] Nucleic Acids Res.
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2703945
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6. Garten Y, Coulet A, Altman RB: Recent progress in automatically extracting information from the pharmacogenomic literature. Pharmacogenomics; 2010 Oct;11(10):1467-89
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Recent progress in automatically extracting information from the pharmacogenomic literature.
  • The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals.
  • In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature.
  • By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports.
  • We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies.
  • Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature.
  • These methods enable extraction of specific types of information and can also provide answers to general, systemic queries.
  • In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications.

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  • (PMID = 21047206.001).
  • [ISSN] 1744-8042
  • [Journal-full-title] Pharmacogenomics
  • [ISO-abbreviation] Pharmacogenomics
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NLM NIH HHS / LM / LM07033; United States / NLM NIH HHS / LM / R01 LM005652; United States / NHGRI NIH HHS / HG / U54 HG004028; United States / NLM NIH HHS / LM / LM05652; United States / NIGMS NIH HHS / GM / GM061374-10S1; United States / NIGMS NIH HHS / GM / U01 GM061374-10S1; United States / NIGMS NIH HHS / GM / GM61374; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / U01 GM061374
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Review
  • [Publication-country] England
  • [Other-IDs] NLM/ NIHMS268321; NLM/ PMC3035632
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7. Coutu MF, Légaré F, Durand MJ, Corbière M, Stacey D, Loisel P, Bainbridge L: Fostering shared decision making by occupational therapists and workers involved in accidents resulting in persistent musculoskeletal disorders: a study protocol. Implement Sci; 2011;6:22
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Fostering shared decision making by occupational therapists and workers involved in accidents resulting in persistent musculoskeletal disorders: a study protocol.
  • BACKGROUND: From many empirical and theoretical points of view, the implementation of shared decision making (SDM) in work rehabilitation for pain due to a musculoskeletal disorder (MSD) is justified but typically the SDM model applies to a one on one encounter between a healthcare provider and a patient and not to an interdisciplinary team.
  • OBJECTIVES: To adapt and implement an SDM program adapted to the realities of work rehabilitation for pain associated with a MSD.
  • More specific objectives are to adapt an SDM program applicable to existing rehabilitation programs, and to evaluate the extent of implementation of the SDM program in four rehabilitation centres.
  • METHOD: For objective one, we will use a mixed perspective combining a theory-based development program/intervention and a user-based perspective.
  • The users are the occupational therapists (OTs) and clinical coordinators.
  • The strategies for developing an SDM program will include consulting the scientific literature and group consensus with clinicians-experts.
  • A sample of convenience of eight OTs, four clinical coordinators and four psychologists all of whom have been working full-time in MSD rehabilitation for more than two years will be recruited from four collaborating rehabilitation centres.
  • For objective two, using the same criteria as for objective one, we will first train eight OTs in SDM.
  • Second, using a descriptive design, the extent to which the SDM program has been implemented will be assessed through observations of the SDM process.
  • The observation data will be triangulated with the dyadic working alliance questionnaire, and findings from a final individual interview with each OT.
  • A total of five patients per trained OT will be recruited, for a total of 40 patients.
  • Patients will be eligible if they have a work-related disability for more than 12 weeks due to musculoskeletal pain and plan to start their work rehabilitation programs.
  • DISCUSSION: This study will be the first evaluation of the program and it is expected that improvements will be made prior to a broader-scale implementation.
  • The ultimate aim is to improve the quality of decision making, patients' quality of life, and reduce the duration of their work-related disability by improving the services offered during the rehabilitation process.
  • [MeSH-major] Accidents, Occupational. Decision Making. Disabled Persons / rehabilitation. Musculoskeletal Diseases / rehabilitation. Occupational Therapy
  • [MeSH-minor] Female. Humans. Male. Program Development. Program Evaluation. Psychometrics. Rehabilitation Centers. Return to Work. Surveys and Questionnaires

  • MedlinePlus Health Information. consumer health - Disabilities.
  • MedlinePlus Health Information. consumer health - Occupational Health.
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  • (PMID = 21414207.001).
  • [ISSN] 1748-5908
  • [Journal-full-title] Implementation science : IS
  • [ISO-abbreviation] Implement Sci
  • [Language] eng
  • [Publication-type] Journal Article; Multicenter Study; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3068973
  • [General-notes] NLM/ Original DateCompleted: 20110714
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8. Garten Y, Altman RB: Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text. BMC Bioinformatics; 2009 Feb 05;10 Suppl 2:S6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text.
  • BACKGROUND: Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes.
  • The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background.
  • The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals.
  • It is challenging, therefore, to identify important associations between drugs and molecular entities--particularly genes and gene variants, and thus these critical connections are often lost.
  • Text mining techniques can allow us to convert the free-style text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded.
  • Availability of full text articles as input into text mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations.
  • RESULTS: Thus, building on a tool called Textpresso, we have created the Pharmspresso tool to assist in identifying important pharmacogenomic facts in full text articles.
  • Pharmspresso parses text to find references to human genes, polymorphisms, drugs and diseases and their relationships.
  • It presents these as a series of marked-up text fragments, in which key concepts are visually highlighted.
  • To evaluate Pharmspresso, we used a gold standard of 45 human-curated articles.
  • Pharmspresso identified 78%, 61%, and 74% of target gene, polymorphism, and drug concepts, respectively.
  • CONCLUSION: Pharmspresso is a text analysis tool that extracts pharmacogenomic concepts from the literature automatically and thus captures our current understanding of gene-drug interactions in a computable form.
  • We have made Pharmspresso available at http://pharmspresso.stanford.edu.

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  • (PMID = 19208194.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NIGMS NIH HHS / GM / GM61374; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NLM NIH HHS / LM / LM007033
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2646239
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9. Hoogendam A, Stalenhoef AF, Robbé PF, Overbeke AJ: Analysis of queries sent to PubMed at the point of care: observation of search behaviour in a medical teaching hospital. BMC Med Inform Decis Mak; 2008;8:42
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Analysis of queries sent to PubMed at the point of care: observation of search behaviour in a medical teaching hospital.
  • BACKGROUND: The use of PubMed to answer daily medical care questions is limited because it is challenging to retrieve a small set of relevant articles and time is restricted.
  • Knowing what aspects of queries are likely to retrieve relevant articles can increase the effectiveness of PubMed searches.
  • The objectives of our study were to identify queries that are likely to retrieve relevant articles by relating PubMed search techniques and tools to the number of articles retrieved and the selection of articles for further reading.
  • METHODS: This was a prospective observational study of queries regarding patient-related problems sent to PubMed by residents and internists in internal medicine working in an Academic Medical Centre.
  • We analyzed queries, search results, query tools (Mesh, Limits, wildcards, operators), selection of abstract and full-text for further reading, using a portal that mimics PubMed.
  • RESULTS: PubMed was used to solve 1121 patient-related problems, resulting in 3205 distinct queries.
  • Abstracts were viewed in 999 (31%) of these queries, and in 126 (39%) of 321 queries using query tools.
  • The average term count per query was 2.5.
  • Abstracts were selected in more than 40% of queries using four or five terms, increasing to 63% if the use of four or five terms yielded 2-161 articles.
  • CONCLUSION: Queries sent to PubMed by physicians at our hospital during daily medical care contain fewer than three terms.
  • Queries using four to five terms, retrieving less than 161 article titles, are most likely to result in abstract viewing.
  • PubMed search tools are used infrequently by our population and are less effective than the use of four or five terms.
  • Methods to facilitate the formulation of precise queries, using more relevant terms, should be the focus of education and research.
  • [MeSH-major] Information Storage and Retrieval / methods. Point-of-Care Systems. PubMed
  • [MeSH-minor] Abstracting and Indexing as Topic. Hospitals, Teaching. Humans. Internal Medicine. Internship and Residency. Medical Subject Headings / utilization. Observation. Periodicals as Topic. Prospective Studies. User-Computer Interface

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  • (PMID = 18816391.001).
  • [ISSN] 1472-6947
  • [Journal-full-title] BMC medical informatics and decision making
  • [ISO-abbreviation] BMC Med Inform Decis Mak
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2567311
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10. Smalheiser NR, Zhou W, Torvik VI: Anne O'Tate: A tool to support user-driven summarization, drill-down and browsing of PubMed search results. J Biomed Discov Collab; 2008 Feb 15;3:2
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Anne O'Tate: A tool to support user-driven summarization, drill-down and browsing of PubMed search results.
  • BACKGROUND: PubMed is designed to provide rapid, comprehensive retrieval of papers that discuss a given topic.
  • However, because PubMed does not organize the search output further, it is difficult for users to grasp an overview of the retrieved literature according to non-topical dimensions, to drill-down to find individual articles relevant to a particular individual's need, or to browse the collection.
  • RESULTS: In this paper, we present Anne O'Tate, a web-based tool that processes articles retrieved from PubMed and displays multiple aspects of the articles to the user, according to pre-defined categories such as the "most important" words found in titles or abstracts; topics; journals; authors; publication years; and affiliations.
  • Clicking on a given item opens a new window that displays all papers that contain that item.
  • One can navigate by drilling down through the categories progressively, e.g., one can first restrict the articles according to author name and then restrict that subset by affiliation.
  • Alternatively, one can expand small sets of articles to display the most closely related articles.
  • We also implemented a novel cluster-by-topic method that generates a concise set of topics covering most of the retrieved articles.
  • CONCLUSION: Anne O'Tate is an integrated, generic tool for summarization, drill-down and browsing of PubMed search results that accommodates a wide range of biomedical users and needs.
  • It can be accessed at 4.
  • Peer review and editorial matters for this article were handled by Aaron Cohen.

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  • (PMID = 18279519.001).
  • [ISSN] 1747-5333
  • [Journal-full-title] Journal of biomedical discovery and collaboration
  • [ISO-abbreviation] J Biomed Discov Collab
  • [Language] ENG
  • [Grant] United States / NLM NIH HHS / LM / R01 LM007292; United States / NLM NIH HHS / LM / R21 LM008364
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC2276193
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