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Items 1 to 10 of about 179
1. Tsuruoka Y, Tsujii J, Ananiadou S: FACTA: a text search engine for finding associated biomedical concepts. Bioinformatics; 2008 Nov 1;24(21):2559-60
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] FACTA: a text search engine for finding associated biomedical concepts.
  • FACTA is a text search engine for MEDLINE abstracts, which is designed particularly to help users browse biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) appearing in the documents retrieved by the query.
  • The concepts are presented to the user in a tabular format and ranked based on the co-occurrence statistics.
  • Unlike existing systems that provide similar functionality, FACTA pre-indexes not only the words but also the concepts mentioned in the documents, which enables the user to issue a flexible query (e.g. free keywords or Boolean combinations of keywords/concepts) and receive the results immediately even when the number of the documents that match the query is very large.
  • The user can also view snippets from MEDLINE to get textual evidence of associations between the query terms and the concepts.
  • The concept IDs and their names/synonyms for building the indexes were collected from several biomedical databases and thesauri, such as UniProt, BioThesaurus, UMLS, KEGG and DrugBank.
  • AVAILABILITY: The system is available at http://www.nactem.ac.uk/software/facta/

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  • (PMID = 18772154.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] ENG
  • [Grant] United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/E004431/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2572701
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2. Yakushiji A, Tateisi Y, Miyao Y, Tsujii J: Event extraction from biomedical papers using a full parser. Pac Symp Biocomput; 2001;:408-19
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Event extraction from biomedical papers using a full parser.
  • We have designed and implemented an information extraction system using a full parser to investigate the plausibility of full analysis of text using general-purpose parser and grammar applied to biomedical domain.
  • We partially solved the problems of full parsing of inefficiency, ambiguity, and low coverage by introducing the preprocessors, and proposed the use of modules that handles partial results of parsing for further improvement.
  • Our approach makes it possible to modularize the system, so that the IE system as a whole becomes easy to be tuned to specific domains, and easy to be maintained and improved by incorporating various techniques of disambiguation, speed up, etc.
  • In preliminary experiment, from 133 argument structures that should be extracted from 97 sentences, we obtained 23% uniquely and 24% with ambiguity.
  • And 20% are extractable from not complete but partial results of full parsing.
  • [MeSH-major] Natural Language Processing
  • [MeSH-minor] Automatic Data Processing. Databases, Factual

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  • (PMID = 11262959.001).
  • [ISSN] 2335-6936
  • [Journal-full-title] Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
  • [ISO-abbreviation] Pac Symp Biocomput
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] Singapore
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3. O'Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S: Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev; 2015 Jan 14;4:5
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Using text mining for study identification in systematic reviews: a systematic review of current approaches.
  • BACKGROUND: The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming.
  • Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved.
  • The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap.
  • Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities.
  • METHODS: Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged?
  • We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings.
  • RESULTS: The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable.
  • On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall).
  • CONCLUSIONS: Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews.
  • The use of text mining as a 'second screener' may also be used cautiously.
  • The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven.
  • In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.

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  • (PMID = 25588314.001).
  • [ISSN] 2046-4053
  • [Journal-full-title] Systematic reviews
  • [ISO-abbreviation] Syst Rev
  • [Language] ENG
  • [Grant] United Kingdom / Medical Research Council / / MR/J005037/1; United Kingdom / Medical Research Council / / MR/L01078X/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't; Review
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC4320539
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4. Tsuruoka Y, Tsujii J, Ananiadou S: Accelerating the annotation of sparse named entities by dynamic sentence selection. BMC Bioinformatics; 2008 Nov 19;9 Suppl 11:S8
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Accelerating the annotation of sparse named entities by dynamic sentence selection.
  • BACKGROUND: Previous studies of named entity recognition have shown that a reasonable level of recognition accuracy can be achieved by using machine learning models such as conditional random fields or support vector machines.
  • However, the lack of training data (i.e. annotated corpora) makes it difficult for machine learning-based named entity recognizers to be used in building practical information extraction systems.
  • RESULTS: This paper presents an active learning-like framework for reducing the human effort required to create named entity annotations in a corpus.
  • In this framework, the annotation work is performed as an iterative and interactive process between the human annotator and a probabilistic named entity tagger.
  • Unlike active learning, our framework aims to annotate all occurrences of the target named entities in the given corpus, so that the resulting annotations are free from the sampling bias which is inevitable in active learning approaches.
  • CONCLUSION: We evaluate our framework by simulating the annotation process using two named entity corpora and show that our approach can reduce the number of sentences which need to be examined by the human annotator.
  • The cost reduction achieved by the framework could be drastic when the target named entities are sparse.
  • [MeSH-major] Information Storage and Retrieval / methods. Pattern Recognition, Automated / methods. Terminology as Topic
  • [MeSH-minor] Algorithms. Artificial Intelligence. Databases, Bibliographic. Natural Language Processing

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  • [Cites] Bioinformatics. 2003;19 Suppl 1:i180-2 [12855455.001]
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  • (PMID = 19025694.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] eng
  • [Grant] United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/E004431/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2586757
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5. Ohta T, Pyysalo S, Kim JD, Tsujii J: A re-evaluation of biomedical named entity-term relations. J Bioinform Comput Biol; 2010 Oct;8(5):917-28
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] A re-evaluation of biomedical named entity-term relations.
  • Text mining can support the interpretation of the enormous quantity of textual data produced in biomedical field.
  • Recent developments in biomedical text mining include advances in the reliability of the recognition of named entities (NEs) such as specific genes and proteins, as well as movement toward richer representations of the associations of NEs.
  • We argue that this shift in representation should be accompanied by the adoption of a more detailed model of the relations holding between NEs and other relevant domain terms.
  • As a step toward this goal, we study NE-term relations with the aim of defining a detailed, broadly applicable set of relation types based on accepted domain standard concepts for use in corpus annotation and domain information extraction approaches.
  • [MeSH-major] Data Mining
  • [MeSH-minor] Computational Biology. Genetic Variation. Genomics / statistics & numerical data. Proteomics / statistics & numerical data. Reference Standards. Terminology as Topic

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  • (PMID = 20981895.001).
  • [ISSN] 1757-6334
  • [Journal-full-title] Journal of bioinformatics and computational biology
  • [ISO-abbreviation] J Bioinform Comput Biol
  • [Language] eng
  • [Publication-type] Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
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6. Tsuruoka Y, McNaught J, Tsujii J, Ananiadou S: Learning string similarity measures for gene/protein name dictionary look-up using logistic regression. Bioinformatics; 2007 Oct 15;23(20):2768-74
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Learning string similarity measures for gene/protein name dictionary look-up using logistic regression.
  • MOTIVATION: One of the bottlenecks of biomedical data integration is variation of terms.
  • Exact string matching often fails to associate a name with its biological concept, i.e.
  • ID or accession number in the database, due to seemingly small differences of names.
  • Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names.
  • However, the accuracy of soft matching highly depends on the similarity measure employed.
  • RESULTS: We used logistic regression for learning a string similarity measure from a dictionary.
  • Experiments using several large-scale gene/protein name dictionaries showed that the logistic regression-based similarity measure outperforms existing similarity measures in dictionary look-up tasks.
  • AVAILABILITY: A dictionary look-up system using the similarity measures described in this article is available at http://text0.mib.man.ac.uk/software/mldic/.
  • [MeSH-major] Artificial Intelligence. Databases, Protein. Genes. Information Storage and Retrieval / methods. Natural Language Processing. Proteins / classification. Terminology as Topic
  • [MeSH-minor] Logistic Models. Regression Analysis

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  • (PMID = 17698493.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] eng
  • [Grant] United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/E004431/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Chemical-registry-number] 0 / Proteins
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7. Ochiai T, Sugitani M, Nishimura K, Noguchi H, Okada T, Ouchi M, Yamada M, Kitajima M, Tsuruoka Y, Takahashi Y, Futagawa S: Impact of orotate phosphoribosyl transferase activity as a predictor of lymph node metastasis in gastric cancer. Oncol Rep; 2005 Oct;14(4):987-92
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Impact of orotate phosphoribosyl transferase activity as a predictor of lymph node metastasis in gastric cancer.
  • Orotate phosphoribosyl transferase (OPRT) is an essential nucleotide metabolic enzyme for cell proliferation and also a key enzyme for conversion of 5-FU to its active form in tumor tissue.
  • The association between tumor OPRT activity and pathophysiological status, including lymph node metastasis [pN+], and the impact of OPRT for predicting pN+ were investigated in gastric cancer.
  • The lymph node status of 73 resectable gastric cancer patients was analyzed preoperatively by computed tomography (CT), ultrasonography and magnetic resonance, and the OPRT activity of collected tumor tissue was measured.
  • Then these data were compared with pathological observation of a surgical lymph node specimen.
  • OPRT activity in the tumor tissue decreased as the depth of invasion increased.
  • An OPRT test demonstrated superior sensitivity and comparable accuracy and sensitivity for predicting pN+, against current imaging diagnoses.
  • Furthermore, the analysis of node negative patients by CT revealed that 80% of false negative patients were retrieved by this OPRT test.
  • Thus, OPRT activity in tumor tissue was a powerful predictor of pN+ in resectable gastric cancer, and the preoperative OPRT test, when it becomes possible, would provide a basis for accurate evaluation of disease status, which is indispensable for the planning of personalized therapy.
  • [MeSH-major] Orotate Phosphoribosyltransferase / metabolism. Stomach Neoplasms / enzymology
  • [MeSH-minor] Aged. Antineoplastic Agents / pharmacology. Cell Proliferation. Female. Fluorouracil / pharmacology. Humans. Lymphatic Metastasis. Magnetic Resonance Imaging. Male. Middle Aged. Neoplasm Invasiveness. Neoplasm Metastasis. Ultrasonography

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  • (PMID = 16142362.001).
  • [ISSN] 1021-335X
  • [Journal-full-title] Oncology reports
  • [ISO-abbreviation] Oncol. Rep.
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] Greece
  • [Chemical-registry-number] 0 / Antineoplastic Agents; EC 2.4.2.10 / Orotate Phosphoribosyltransferase; U3P01618RT / Fluorouracil
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8. Ananiadou S, Kell DB, Tsujii J: Text mining and its potential applications in systems biology. Trends Biotechnol; 2006 Dec;24(12):571-9
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Text mining and its potential applications in systems biology.
  • With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required.
  • Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this.
  • By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.
  • [MeSH-major] Information Storage and Retrieval / methods. Models, Biological. Natural Language Processing. Publications. Research Design. Systems Biology / methods
  • [MeSH-minor] Artificial Intelligence

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  • (PMID = 17045684.001).
  • [ISSN] 0167-7799
  • [Journal-full-title] Trends in biotechnology
  • [ISO-abbreviation] Trends Biotechnol.
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't; Review
  • [Publication-country] England
  • [Number-of-references] 95
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9. Kano Y, Nguyen N, Saetre R, Yoshida K, Miyao Y, Tsuruoka Y, Matsubayashi Y, Ananiadou S, Tsujii J: Filling the gaps between tools and users: a tool comparator, using protein-protein interaction as an example. Pac Symp Biocomput; 2008;:616-27
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Filling the gaps between tools and users: a tool comparator, using protein-protein interaction as an example.
  • Recently, several text mining programs have reached a near-practical level of performance.
  • Some systems are already being used by biologists and database curators.
  • However, it has also been recognized that current Natural Language Processing (NLP) and Text Mining (TM) technology is not easy to deploy, since research groups tend to develop systems that cater specifically to their own requirements.
  • One of the major reasons for the difficulty of deployment of NLP/TM technology is that re-usability and interoperability of software tools are typically not considered during development.
  • While some effort has been invested in making interoperable NLP/TM toolkits, the developers of end-to-end systems still often struggle to reuse NLP/TM tools, and often opt to develop similar programs from scratch instead.
  • This is particularly the case in BioNLP, since the requirements of biologists are so diverse that NLP tools have to be adapted and re-organized in a much more extensive manner than was originally expected.
  • Although generic frameworks like UIMA (Unstructured Information Management Architecture) provide promising ways to solve this problem, the solution that they provide is only partial.
  • In order for truly interoperable toolkits to become a reality, we also need sharable type systems and a developer-friendly environment for software integration that includes functionality for systematic comparisons of available tools, a simple I/O interface, and visualization tools.
  • In this paper, we describe such an environment that was developed based on UIMA, and we show its feasibility through our experience in developing a protein-protein interaction (PPI) extraction system.
  • [MeSH-major] Computational Biology. Protein Interaction Mapping / statistics & numerical data
  • [MeSH-minor] Information Storage and Retrieval. Natural Language Processing

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  • (PMID = 18229720.001).
  • [ISSN] 2335-6936
  • [Journal-full-title] Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
  • [ISO-abbreviation] Pac Symp Biocomput
  • [Language] eng
  • [Publication-type] Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] Singapore
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10. Okazaki N, Ananiadou S, Tsujii J: Building a high-quality sense inventory for improved abbreviation disambiguation. Bioinformatics; 2010 May 1;26(9):1246-53
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Building a high-quality sense inventory for improved abbreviation disambiguation.
  • MOTIVATION: The ultimate goal of abbreviation management is to disambiguate every occurrence of an abbreviation into its expanded form (concept or sense).
  • To collect expanded forms for abbreviations, previous studies have recognized abbreviations and their expanded forms in parenthetical expressions of bio-medical texts.
  • However, expanded forms extracted by abbreviation recognition are mixtures of concepts/senses and their term variations.
  • Consequently, a list of expanded forms should be structured into a sense inventory, which provides possible concepts or senses for abbreviation disambiguation.
  • RESULTS: A sense inventory is a key to robust management of abbreviations.
  • Therefore, we present a supervised approach for clustering expanded forms.
  • The experimental result reports 0.915 F1 score in clustering expanded forms.
  • We then investigate the possibility of conflicts of protein and gene names with abbreviations.
  • Finally, an experiment of abbreviation disambiguation on the sense inventory yielded 0.984 accuracy and 0.986 F1 score using the dataset obtained from MEDLINE abstracts.
  • AVAILABILITY: The sense inventory and disambiguator of abbreviations are accessible at http://www.nactem.ac.uk/software/acromine/ and http://www.nactem.ac.uk/software/acromine_disambiguation/.

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  • (PMID = 20360059.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] ENG
  • [Grant] United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/E004431/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2859134
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