Sophia Ananiadou

Summary

Affiliation: University of Manchester
Country: UK

Publications

  1. pmc Anatomical entity mention recognition at literature scale
    Sampo Pyysalo
    National Centre for Text Mining and University of Manchester, Manchester, UK
    Bioinformatics 30:868-75. 2014
  2. pmc U-Compare bio-event meta-service: compatible BioNLP event extraction services
    Yoshinobu Kano
    Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4 1 8 Honcho, Kawaguchi, Saitama, 332 0012, Japan
    BMC Bioinformatics 12:481. 2011
  3. ncbi request reprint Text mining and its potential applications in systems biology
    Sophia Ananiadou
    School of Computer Science, National Centre for Text Mining, The Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7ND, UK
    Trends Biotechnol 24:571-9. 2006
  4. doi request reprint Event extraction for systems biology by text mining the literature
    Sophia Ananiadou
    University of Manchester, Manchester M13 9PL, UK
    Trends Biotechnol 28:381-90. 2010
  5. ncbi request reprint Text mining and ontologies in biomedicine: making sense of raw text
    Irena Spasic
    School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK
    Brief Bioinform 6:239-51. 2005
  6. pmc A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text
    Makoto Miwa
    The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
    Bioinformatics 29:i44-52. 2013
  7. pmc Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011
    Sampo Pyysalo
    School of Computer Science, University of Manchester, Manchester, UK
    BMC Bioinformatics 13:S2. 2012
  8. pmc Accelerating the annotation of sparse named entities by dynamic sentence selection
    Yoshimasa Tsuruoka
    School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester M17DN, UK
    BMC Bioinformatics 9:S8. 2008
  9. pmc Extracting semantically enriched events from biomedical literature
    Makoto Miwa
    The National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK
    BMC Bioinformatics 13:108. 2012
  10. pmc A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data
    Angelo Restificar
    National Center for Text Mining and School of Computer Science, The University of Manchester, Manchester, M1 7DN, UK
    BMC Med Inform Decis Mak 13:S6. 2013

Collaborators

Detail Information

Publications41

  1. pmc Anatomical entity mention recognition at literature scale
    Sampo Pyysalo
    National Centre for Text Mining and University of Manchester, Manchester, UK
    Bioinformatics 30:868-75. 2014
    ....
  2. pmc U-Compare bio-event meta-service: compatible BioNLP event extraction services
    Yoshinobu Kano
    Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4 1 8 Honcho, Kawaguchi, Saitama, 332 0012, Japan
    BMC Bioinformatics 12:481. 2011
    ..While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes...
  3. ncbi request reprint Text mining and its potential applications in systems biology
    Sophia Ananiadou
    School of Computer Science, National Centre for Text Mining, The Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7ND, UK
    Trends Biotechnol 24:571-9. 2006
    ..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...
  4. doi request reprint Event extraction for systems biology by text mining the literature
    Sophia Ananiadou
    University of Manchester, Manchester M13 9PL, UK
    Trends Biotechnol 28:381-90. 2010
    ..The approaches described will be of considerable value in associating particular pathways and their components with higher-order physiological properties, including disease states...
  5. ncbi request reprint Text mining and ontologies in biomedicine: making sense of raw text
    Irena Spasic
    School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK
    Brief Bioinform 6:239-51. 2005
    ..The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine...
  6. pmc A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text
    Makoto Miwa
    The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
    Bioinformatics 29:i44-52. 2013
    ..These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge...
  7. pmc Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011
    Sampo Pyysalo
    School of Computer Science, University of Manchester, Manchester, UK
    BMC Bioinformatics 13:S2. 2012
    ..The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties...
  8. pmc Accelerating the annotation of sparse named entities by dynamic sentence selection
    Yoshimasa Tsuruoka
    School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester M17DN, UK
    BMC Bioinformatics 9:S8. 2008
    ..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...
  9. pmc Extracting semantically enriched events from biomedical literature
    Makoto Miwa
    The National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK
    BMC Bioinformatics 13:108. 2012
    ..The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them...
  10. pmc A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data
    Angelo Restificar
    National Center for Text Mining and School of Computer Science, The University of Manchester, Manchester, M1 7DN, UK
    BMC Med Inform Decis Mak 13:S6. 2013
    ..The appropriateness is measured by the degree to which they are consistent with the user-supplied sample documents D'...
  11. pmc Normalizing biomedical terms by minimizing ambiguity and variability
    Yoshimasa Tsuruoka
    School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester, M1 7DN, UK
    BMC Bioinformatics 9:S2. 2008
    ..The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach...
  12. pmc Automatic extraction of angiogenesis bioprocess from text
    Xinglong Wang
    National Centre for Text Mining, University of Manchester, Manchester, AstraZeneca, Alderley Park, UK
    Bioinformatics 27:2730-7. 2011
    ..Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find...
  13. pmc Using workflows to explore and optimise named entity recognition for chemistry
    Balakrishna Kolluru
    National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, United Kingdom
    PLoS ONE 6:e20181. 2011
    ..35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR...
  14. ncbi request reprint Proximity-based frameworks for generating embeddings from multi-output data
    Tingting Mu
    National Centre for Text Mining NaCTeM, School of Computer Science, University of Manchester, Manchester, United Kingdom
    IEEE Trans Pattern Anal Mach Intell 34:2216-32. 2012
    ..The effectiveness of our proposed methodologies is demonstrated with experiments with document collections for multilabel text categorization from the natural language processing domain...
  15. pmc Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature
    Xinglong Wang
    National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK
    BMC Bioinformatics 12:S11. 2011
    ..ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles...
  16. pmc Enriching a biomedical event corpus with meta-knowledge annotation
    Paul Thompson
    National Centre for Text Mining, Manchester Interdisciplinary Biocentre, School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
    BMC Bioinformatics 12:393. 2011
    ....
  17. pmc Construction of an annotated corpus to support biomedical information extraction
    Paul Thompson
    National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
    BMC Bioinformatics 10:349. 2009
    ..Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources...
  18. ncbi request reprint Learning string similarity measures for gene/protein name dictionary look-up using logistic regression
    Yoshimasa Tsuruoka
    School of Computer Science, The University of Manchester, Manchester, UK
    Bioinformatics 23:2768-74. 2007
    ..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...
  19. pmc Negated bio-events: analysis and identification
    Raheel Nawaz
    National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
    BMC Bioinformatics 14:14. 2013
    ..In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations...
  20. pmc Wide coverage biomedical event extraction using multiple partially overlapping corpora
    Makoto Miwa
    The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
    BMC Bioinformatics 14:175. 2013
    ....
  21. pmc ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
    Ioannis Korkontzelos
    National Centre for Text Mining and School of Computer Science, The University of Manchester, Manchester M1 7DN, UK
    BMC Med Inform Decis Mak 12:S3. 2012
    ..In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols...
  22. pmc Event extraction across multiple levels of biological organization
    Sampo Pyysalo
    National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK
    Bioinformatics 28:i575-i581. 2012
    ..However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability...
  23. pmc Argo: an integrative, interactive, text mining-based workbench supporting curation
    Rafal Rak
    National Centre for Text Mining and School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
    Database (Oxford) 2012:bas010. 2012
    ..As a use case, we demonstrate the functionality of an in-built manual annotation editor that is well suited for in-text corpus annotation tasks. DATABASE URL: http://www.nactem.ac.uk/Argo...
  24. pmc How to make the most of NE dictionaries in statistical NER
    Yutaka Sasaki
    School of Computer Science, University of Manchester, 131 Princess Street, Manchester M17DN, UK
    BMC Bioinformatics 9:S5. 2008
    ..However, in reality, the retraining of NER models is required to achieve this. We chose protein name recognition as a case study because it most suffers the problems related to heavy term variation and ambiguity...
  25. pmc FACTA: a text search engine for finding associated biomedical concepts
    Yoshimasa Tsuruoka
    School of Computer Science, The University of Manchester, Manchester, UK
    Bioinformatics 24:2559-60. 2008
    ..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...
  26. ncbi request reprint The value of an in-domain lexicon in genomics QA
    Yutaka Sasaki
    National Centre for Text Mining, School of Computer Science, University of Manchester, MIB, 131 Princess Street, Manchester M17DN, United Kingdom
    J Bioinform Comput Biol 8:147-61. 2010
    ..Experiments on the genomics QA data set show that question analysis using the BioLexicon performs slightly better than that using n-grams and the UMLS Specialist Lexicon...
  27. ncbi request reprint Clinical text classification under the Open and Closed Topic Assumptions
    Yutaka Sasaki
    School of Computer Science, University of Manchester MIB, 131 Princess Street, Manchester, M1 7DN, UK
    Int J Data Min Bioinform 3:299-313. 2009
    ....
  28. pmc Disambiguating the species of biomedical named entities using natural language parsers
    Xinglong Wang
    National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK
    Bioinformatics 26:661-7. 2010
    ....
  29. pmc BioCause: Annotating and analysing causality in the biomedical domain
    Claudiu Mihăilă
    The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
    BMC Bioinformatics 14:2. 2013
    ..A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining...
  30. pmc Deploying and sharing U-Compare workflows as web services
    Georgios Kontonatsios
    National Centre for Text Mining and School of Computer Science, The University of Manchester, Manchester, M1 7DN, UK
    J Biomed Semantics 4:7. 2013
    ..However, the resulting workflows are standalone applications, i.e., software tools that run and are accessible only via a local machine, and that can only be run with the U-Compare platform...
  31. pmc The BioLexicon: a large-scale terminological resource for biomedical text mining
    Paul Thompson
    School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
    BMC Bioinformatics 12:397. 2011
    ..Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events...
  32. pmc Named entity recognition for bacterial Type IV secretion systems
    Sophia Ananiadou
    School of Computer Science, University of Manchester, Manchester, United Kingdom
    PLoS ONE 6:e14780. 2011
    ..Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents...
  33. doi request reprint Automatic extraction of microorganisms and their habitats from free text using text mining workflows
    Balakrishna Kolluru
    National Centre for Text Mining, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
    J Integr Bioinform 8:184. 2011
    ..We also conjecture that pdf-to-text conversion can be quite noisy and this implicitly affects any sentence-based relation extraction algorithms...
  34. ncbi request reprint MaSTerClass: a case-based reasoning system for the classification of biomedical terms
    Irena Spasic
    School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK
    Bioinformatics 21:2748-58. 2005
    ..The extracted terms still need to be correctly positioned relative to other terms in the network. Classification as a means of semantic typing represents the first step in updating a semantic network with new terms...
  35. ncbi request reprint Recognising discourse causality triggers in the biomedical domain
    Claudiu Mihăilă
    The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
    J Bioinform Comput Biol 11:1343008. 2013
    ..The best performance of 79.35% F-score is achieved by CRFs when using all three feature types. ..
  36. pmc Boosting automatic event extraction from the literature using domain adaptation and coreference resolution
    Makoto Miwa
    The National Centre for Text Mining NaCTeM, UK
    Bioinformatics 28:1759-65. 2012
    ..Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage...
  37. ncbi request reprint Building an abbreviation dictionary using a term recognition approach
    Naoaki Okazaki
    Graduate School of Information Science and Technology, The University of Tokyo 7 3 1 Hongo, Bunkyo ku, Tokyo 113 8651, Japan
    Bioinformatics 22:3089-95. 2006
    ..Acronyms result from a highly productive type of term variation and trigger the need for an acronym dictionary to establish associations between acronyms and their expanded forms...
  38. ncbi request reprint Using automatically learnt verb selectional preferences for classification of biomedical terms
    Irena Spasic
    Department of Chemistry, UMIST, Faraday Building, P O Box 88, Sackville Street, Manchester M60 1QD, UK
    J Biomed Inform 37:483-97. 2004
    ..The most similar candidate class is predicted for the given term. The similarity measure used for this purpose combines contextual, lexical, and syntactic properties of terms...
  39. ncbi request reprint Terminology-driven mining of biomedical literature
    Goran Nenadic
    Computer Science, University of Salford, Salford M5 4WT, UK
    Bioinformatics 19:938-43. 2003
    ..Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing...
  40. ncbi request reprint Terminology-driven literature mining and knowledge acquisition in biomedicine
    Goran Nenadic
    Computer Science Department, University of Salford, Salford, UK
    Int J Med Inform 67:33-48. 2002
    ..Through KA examples, we illustrate the way in which literature mining techniques can be utilised for knowledge discovery from documents...