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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. Krallinger M, Valencia A, Hirschman L: Linking genes to literature: text mining, information extraction, and retrieval applications for biology. Genome Biol; 2008;9 Suppl 2:S8
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Linking genes to literature: text mining, information extraction, and retrieval applications for biology.
  • Efficient access to information contained in online scientific literature collections is essential for life science research, playing a crucial role from the initial stage of experiment planning to the final interpretation and communication of the results.
  • The biological literature also constitutes the main information source for manual literature curation used by expert-curated databases.
  • Following the increasing popularity of web-based applications for analyzing biological data, new text-mining and information extraction strategies are being implemented.
  • These systems exploit existing regularities in natural language to extract biologically relevant information from electronic texts automatically.
  • The aim of the BioCreative challenge is to promote the development of such tools and to provide insight into their performance.
  • This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the following: the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications.
  • The current trend in biomedical text mining points toward an increasing diversification in terms of application types and techniques, together with integration of domain-specific resources such as ontologies.
  • Additional descriptions of some of the systems discussed here are available on the internet http://zope.bioinfo.cnio.es/bionlp_tools/.
  • [MeSH-major] Computational Biology / methods. Databases, Bibliographic. Genes. Information Storage and Retrieval
  • [MeSH-minor] Semantics. Terminology as Topic

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  • (PMID = 18834499.001).
  • [ISSN] 1474-760X
  • [Journal-full-title] Genome biology
  • [ISO-abbreviation] Genome Biol.
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Review
  • [Publication-country] England
  • [Number-of-references] 103
  • [Other-IDs] NLM/ PMC2559992
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3. Boyack KW, Newman D, Duhon RJ, Klavans R, Patek M, Biberstine JR, Schijvenaars B, Skupin A, Ma N, Börner K: Clustering more than two million biomedical publications: comparing the accuracies of nine text-based similarity approaches. PLoS One; 2011 Mar 17;6(3):e18029
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Clustering more than two million biomedical publications: comparing the accuracies of nine text-based similarity approaches.
  • BACKGROUND: We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents.
  • Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis.
  • The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results.
  • Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.
  • METHODOLOGY: We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings.
  • The nine approaches were comprised of five different analytical techniques with two data sources.
  • The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models--BM25 and PMRA (PubMed Related Articles).
  • The two data sources were a) MeSH subject headings, and b) words from titles and abstracts.
  • Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering.
  • Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.
  • CONCLUSIONS: PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts.
  • Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts.

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  • (PMID = 21437291.001).
  • [ISSN] 1932-6203
  • [Journal-full-title] PloS one
  • [ISO-abbreviation] PLoS ONE
  • [Language] ENG
  • [Grant] United States / NHLBI NIH HHS / HL / HHSN268200900053C; United States / PHS HHS / / HHSN268200900053C
  • [Publication-type] Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3060097
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Advertisement
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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