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Semantic Mining of Languages in Biology and Medicine

Edited by: Jong Park, Sampo Pyysalo, and Fabio Rinaldi

The Semantic Mining of Languages in Biology and Medicine thematic series focuses on representation and mining of various languages used in biology and medicine, and in associated disciplines such as chemistry, biodiversity, environmental sciences, pharmacology, etc. The scope and ideas for this series are rooted in two workshops that have been taking place from 2005. The International Symposium on Languages in Biology and Medicine (LBM) is a biennial interdisciplinary forum that brings together researchers in biology, chemistry, medicine, public health and informatics to discuss and exploit cutting edge language technologies.

  1. Semantic Category Disambiguation (SCD) is the task of assigning the appropriate semantic category to given spans of text from a fixed set of candidate categories, for example Protein to “Fibrin”. SCD is relevant ...

    Authors: Pontus Stenetorp, Sampo Pyysalo, Sophia Ananiadou and Jun’ichi Tsujii
    Citation: Journal of Biomedical Semantics 2014 5:26
  2. Hospital documents contain free text describing the most important facts relating to patients and their illnesses. These documents are written in specific language containing medical terminology related to hos...

    Authors: Małgorzata Marciniak and Agnieszka Mykowiecka
    Citation: Journal of Biomedical Semantics 2014 5:24
  3. Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to commun...

    Authors: Tobias Kuhn, Mate Levente Nagy, ThaiBinh Luong and Michael Krauthammer
    Citation: Journal of Biomedical Semantics 2014 5:10
  4. Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. D...

    Authors: Aron Henriksson, Hans Moen, Maria Skeppstedt, Vidas Daudaravičius and Martin Duneld
    Citation: Journal of Biomedical Semantics 2014 5:6
  5. Identifying phrases that refer to particular concept types is a critical step in extracting information from documents. Provided with annotated documents as training data, supervised machine learning can autom...

    Authors: Manabu Torii, Kavishwar Wagholikar and Hongfang Liu
    Citation: Journal of Biomedical Semantics 2014 5:3
  6. Natural human languages show a power law behaviour in which word frequency (in any large enough corpus) is inversely proportional to word rank - Zipf’s law. We have therefore asked whether similar power law be...

    Authors: Leila R Kalankesh, John P New, Patricia G Baker and Andy Brass
    Citation: Journal of Biomedical Semantics 2014 5:2
  7. In order to perform research on the information contained in Electronic Patient Records (EPRs), access to the data itself is needed. This is often very difficult due to confidentiality regulations. The data se...

    Authors: Hercules Dalianis and Sumithra Velupillai
    Citation: Journal of Biomedical Semantics 2010 1:6
  8. Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensi...

    Authors: Hutchatai Chanlekha and Nigel Collier
    Citation: Journal of Biomedical Semantics 2010 1:3

Annual Journal Metrics

  • 2022 Citation Impact
    1.9 - 2-year Impact Factor
    2.6 - 5-year Impact Factor
    0.870 - SNIP (Source Normalized Impact per Paper)
    0.697 - SJR (SCImago Journal Rank)

    2023 Speed
    23 days submission to first editorial decision for all manuscripts (Median)
    265 days submission to accept (Median)

    2023 Usage 
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