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Items 1 to 10 of about 11
1. Rodriguez-Esteban R: Biomedical text mining and its applications. PLoS Comput Biol; 2009 Dec;5(12):e1000597
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Biomedical text mining and its applications.
  • [MeSH-major] Biomedical Research / methods. Data Mining / methods. Internet. Natural Language Processing. Search Engine / methods. Software

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  • (PMID = 20041219.001).
  • [ISSN] 1553-7358
  • [Journal-full-title] PLoS computational biology
  • [ISO-abbreviation] PLoS Comput. Biol.
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC2791166
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2. Simpson MS, Demner-Fushman D, Thoma GR: Evaluating the Importance of Image-related Text for Ad-hoc and Case-based Biomedical Article Retrieval. AMIA Annu Symp Proc; 2010 Nov 13;2010:752-6
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  • [Title] Evaluating the Importance of Image-related Text for Ad-hoc and Case-based Biomedical Article Retrieval.
  • Images and their associated text are an essential source of information in biomedical articles.
  • However, their use in providing evidence for clinical case descriptions has yet to be evaluated in the context of information retrieval.
  • Given the complexity of case-based document retrieval, understanding the importance of images and image-related text is critical for future research into text- and content-based approaches to this problem.
  • In this study, we compare the extent to which image-related text is useful in facilitating document retrieval for both case-based information requests and ad-hoc clinical questions in the domain of family practice.
  • We show that case-based document retrieval is significantly improved with the use of image-related text whereas retrieval for clinical questions is largely unaffected.
  • This suggests that visual evidence is more relevant for the case descriptions used in our study than the clinical questions.

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  • (PMID = 21347079.001).
  • [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/ PMC3041350
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3. Yu H, Agarwal S, Johnston M, Cohen A: Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension. J Biomed Discov Collab; 2009;4:1
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  • [Title] Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension.
  • BACKGROUND: Biomedical scientists need to access figures to validate research facts and to formulate or to test novel research hypotheses.
  • However, figures are difficult to comprehend without associated text (e.g., figure legend and other reference text).
  • We are developing automated systems to extract the relevant explanatory information along with figures extracted from full text articles.
  • Such systems could be very useful in improving figure retrieval and in reducing the workload of biomedical scientists, who otherwise have to retrieve and read the entire full-text journal article to determine which figures are relevant to their research.
  • As a crucial step, we studied the importance of associated text in biomedical figure comprehension.
  • METHODS: Twenty subjects evaluated three figure-text combinations: figure+legend, figure+legend+title+abstract, and figure+full-text.
  • Using a Likert scale, each subject scored each figure+text according to the extent to which the subject thought he/she understood the meaning of the figure and the confidence in providing the assigned score.
  • Additionally, each subject entered a free text summary for each figure-text.
  • We identified missing information using indicator words present within the text summaries.
  • Both the Likert scores and the missing information were statistically analyzed for differences among the figure-text types.
  • We also evaluated the quality of text summaries with the text-summarization evaluation method the ROUGE score.
  • RESULTS: Our results showed statistically significant differences in figure comprehension when varying levels of text were provided.
  • When the full-text article is not available, presenting just the figure+legend left biomedical researchers lacking 39-68% of the information about a figure as compared to having complete figure comprehension; adding the title and abstract improved the situation, but still left biomedical researchers missing 30% of the information.
  • When the full-text article is available, figure comprehension increased to 86-97%; this indicates that researchers felt that only 3-14% of the necessary information for full figure comprehension was missing when full text was available to them.
  • Clearly there is information in the abstract and in the full text that biomedical scientists deem important for understanding the figures that appear in full-text biomedical articles.
  • CONCLUSION: We conclude that the texts that appear in full-text biomedical articles are useful for understanding the meaning of a figure, and an effective figure-mining system needs to unlock the information beyond figure legend.
  • Our work provides important guidance to the figure mining systems that extract information only from figure and figure legend.

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  • (PMID = 19126221.001).
  • [ISSN] 1747-5333
  • [Journal-full-title] Journal of biomedical discovery and collaboration
  • [ISO-abbreviation] J Biomed Discov Collab
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2631451
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4. Kim D, Yu H: Figure text extraction in biomedical literature. PLoS One; 2011 Jan 13;6(1):e15338
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Figure text extraction in biomedical literature.
  • BACKGROUND: Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge.
  • However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures.
  • Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently.
  • Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures.
  • Little research, however, has been conducted exploring text extraction from biomedical figures.
  • METHODOLOGY: We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles.
  • We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction.
  • We first developed image preprocessing to enhance image quality and to improve text localization.
  • Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition.
  • Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons.
  • RESULTS/CONCLUSIONS: The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction.
  • When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score.
  • FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction.
  • In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search.

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  • (PMID = 21249186.001).
  • [ISSN] 1932-6203
  • [Journal-full-title] PloS one
  • [ISO-abbreviation] PLoS ONE
  • [Language] ENG
  • [Grant] United States / NCRR NIH HHS / RR / R21 RR024933; United States / NCRR NIH HHS / RR / 1R21RR024933
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3020938
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5. Divoli A, Wooldridge MA, Hearst MA: Full text and figure display improves bioscience literature search. PLoS One; 2010;5(4):e9619
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Full text and figure display improves bioscience literature search.
  • When reading bioscience journal articles, many researchers focus attention on the figures and their captions.
  • This observation led to the development of the BioText literature search engine, a freely available Web-based application that allows biologists to search over the contents of Open Access Journals, and see figures from the articles displayed directly in the search results.
  • This article presents a qualitative assessment of this system in the form of a usability study with 20 biologist participants using and commenting on the system.
  • 19 out of 20 participants expressed a desire to use a bioscience literature search engine that displays articles' figures alongside the full text search results.
  • 15 out of 20 participants said they would use a caption search and figure display interface either frequently or sometimes, while 4 said rarely and 1 said undecided.
  • 10 out of 20 participants said they would use a tool for searching the text of tables and their captions either frequently or sometimes, while 7 said they would use it rarely if at all, 2 said they would never use it, and 1 was undecided.
  • This study found evidence, supporting results of an earlier study, that bioscience literature search systems such as PubMed should show figures from articles alongside search results.
  • It also found evidence that full text and captions should be searched along with the article title, metadata, and abstract.
  • Finally, for a subset of users and information needs, allowing for explicit search within captions for figures and tables is a useful function, but it is not entirely clear how to cleanly integrate this within a more general literature search interface.
  • Such a facility supports Open Access publishing efforts, as it requires access to full text of documents and the lifting of restrictions in order to show figures in the search interface.
  • [MeSH-major] Computer Graphics / trends. Databases, Bibliographic / trends. Information Storage and Retrieval / trends. Search Engine
  • [MeSH-minor] Abstracting and Indexing as Topic. PubMed. Publications. User-Computer Interface

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  • (PMID = 20418942.001).
  • [ISSN] 1932-6203
  • [Journal-full-title] PloS one
  • [ISO-abbreviation] PLoS ONE
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC2854679
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6. Kim D, Yu H: Hierarchical image classification in the bioscience literature. AMIA Annu Symp Proc; 2009 Nov 14;2009:327-31
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Hierarchical image classification in the bioscience literature.
  • Our previous work has shown that images appearing in bioscience articles can be classified into five types: Gel-Image, Image-of-Thing, Graph, Model, and Mix.
  • For this paper, we explored and analyzed features strongly associated with each image type and developed a hierarchical image classification approach for classifying an image into one of the five types.
  • First, we applied texture features to separate images into two groups:.
  • 1) a texture group comprising Gel Image, Image-of-Thing, and Mix, and 2) a non-texture group comprising Graph and Model.
  • We then applied entropy, skewness, and uniformity for the first group, and edge difference, uniformity, and smoothness for the second group to classify images into specific types.
  • Our results show that hierarchical image classification accurately divided images into the two groups during the initial classification and that the overall accuracy of the image classification was higher than that of our previous approach.
  • In particular, the recall of hierarchical image classification was greatly improved due to the high accuracy of the initial classification.

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  • [Cites] Bioinformatics. 2006 Jul 15;22(14):e446-53 [16873506.001]
  • [Cites] Bioinformatics. 2006 Jul 15;22(14):e547-56 [16873519.001]
  • [Cites] Bioinformatics. 2008 Sep 1;24(17):1968-70 [18614584.001]
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  • (PMID = 20351874.001).
  • [ISSN] 1942-597X
  • [Journal-full-title] AMIA ... Annual Symposium proceedings. AMIA Symposium
  • [ISO-abbreviation] AMIA Annu Symp Proc
  • [Language] ENG
  • [Grant] United States / NCRR NIH HHS / RR / R21 RR024933; United States / NCRR NIH HHS / RR / 1R21RR024933-01A1
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC2815366
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7. Sarkar IN: Biomedical informatics and translational medicine. J Transl Med; 2010 Feb 26;8:22
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Biomedical informatics and translational medicine.
  • Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine.
  • To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies.
  • Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine.
  • Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

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  • (PMID = 20187952.001).
  • [ISSN] 1479-5876
  • [Journal-full-title] Journal of translational medicine
  • [ISO-abbreviation] J Transl Med
  • [Language] ENG
  • [Grant] United States / NLM NIH HHS / LM / R01 LM009725
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.; Review
  • [Publication-country] England
  • [Number-of-references] 220
  • [Other-IDs] NLM/ PMC2837642
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8. Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW 3rd, Su AI: BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol; 2009;10(11):R130
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources.
  • Online gene annotation resources are indispensable for analysis of genomics data.
  • However, the landscape of these online resources is highly fragmented, and scientists often visit dozens of these sites for each gene in a candidate gene list.
  • Here, we introduce BioGPS http://biogps.gnf.org, a centralized gene portal for aggregating distributed gene annotation resources.
  • Moreover, BioGPS embraces the principle of community intelligence, enabling any user to easily and directly contribute to the BioGPS platform.

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  • (PMID = 19919682.001).
  • [ISSN] 1474-760X
  • [Journal-full-title] Genome biology
  • [ISO-abbreviation] Genome Biol.
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / GM083924
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3091323
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9. Kim S, Lamkin S, Duncan P: Caption-based topical descriptors for microscopic images as published in academic papers. Health Info Libr J; 2010 Sep;27(3):235-43
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Caption-based topical descriptors for microscopic images as published in academic papers.
  • BACKGROUND: Visual findings summarized in the figures and tables of academic papers are invaluable sources for biomedical researchers.
  • Captions associated with the visual findings are often neglected while retrieving biomedical images in published academic papers.
  • OBJECTIVES: This study is to assess caption-based topical descriptors for microscopic images of breast neoplasms, as published in academic papers retrieved through the PubMed Central database.
  • METHOD: Human indexers as well as an automatic keyword finder called TAPoR generated the topical descriptors from collected captions.
  • The study then compared the human-generated descriptors to machine-generated descriptors.
  • Finally, a set of core descriptors was developed from both sets and automatically mapped into the Unified Medical Language System's (UMLS) Metathesaurus through a MetaMap Transfer engine.
  • RESULTS: Major topical descriptors included histologic disease names, laboratory procedures, genetic functions and components.
  • Human indexers provided more relevant descriptors than TAPoR.
  • The UMLS Metathesaurus identified several semantic types including Indicator, Reagent, or Diagnostic Aid; Organic Chemical; Laboratory Procedure; Spatial Concept; Qualitative Concept; and Quantitative Concept.
  • DISCUSSION: The findings suggest that caption-based descriptors can complement title or abstract-based literature indexing for figure image retrieval in articles.
  • With respect to forming a metadata framework for online microscopic image description, the semantic types can be used as a core metadata set.
  • In this regard, this finding can be used for standardising a microscopic image description protocol to train medical students.
  • CONCLUSIONS: It is incumbent upon libraries and other information agencies to promote and maintain an interest in the opportunities and challenges associated with biomedical imaging.
  • [MeSH-major] Abstracting and Indexing as Topic / classification. Manuscripts, Medical. Microscopy / classification. Photography / classification. Publishing. Unified Medical Language System / classification
  • [MeSH-minor] Breast Neoplasms / pathology. Diagnostic Imaging / classification. Female. Humans. Information Storage and Retrieval / methods

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  • (PMID = 20712718.001).
  • [ISSN] 1471-1842
  • [Journal-full-title] Health information and libraries journal
  • [ISO-abbreviation] Health Info Libr J
  • [Language] eng
  • [Grant] United States / NCRR NIH HHS / RR / P20 RR016481; United States / NCRR NIH HHS / RR / P20 RR016481-04; United States / NCRR NIH HHS / RR / P20RR-16481
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ NIHMS227102; NLM/ PMC2933792
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10. 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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