Location of Repository

Detection of Gene Interactions Based on Syntactic Relations

By Mi-Young Kim


Interactions between proteins and genes are considered essential in the description of biomolecular phenomena, and networks of interactions are applied in a system's biology approach. Recently, many studies have sought to extract information from biomolecular text using natural language processing technology. Previous studies have asserted that linguistic information is useful for improving the detection of gene interactions. In particular, syntactic relations among linguistic information are good for detecting gene interactions. However, previous systems give a reasonably good precision but poor recall. To improve recall without sacrificing precision, this paper proposes a three-phase method for detecting gene interactions based on syntactic relations. In the first phase, we retrieve syntactic encapsulation categories for each candidate agent and target. In the second phase, we construct a verb list that indicates the nature of the interaction between pairs of genes. In the last phase, we determine direction rules to detect which of two genes is the agent or target. Even without biomolecular knowledge, our method performs reasonably well using a small training dataset. While the first phase contributes to improve recall, the second and third phases contribute to improve precision. In the experimental results using ICML 05 Workshop on Learning Language in Logic (LLL05) data, our proposed method gave an F-measure of 67.2% for the test data, significantly outperforming previous methods. We also describe the contribution of each phase to the performance

Topics: Research Article
Publisher: Hindawi Publishing Corporation
OAI identifier: oai:pubmedcentral.nih.gov:2277490
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles



    1. (2007). A systematic exploration of the feature space for relation extraction,” in
    2. (1999). Automatic extraction of biological information from scientific text: protein-protein interactions,”
    3. (2001). Bidirectional incremental parsing for automatic pathway identification with combinatory categorial grammar,”
    4. (2004). Classifying semantic relations in bioscience texts,”
    5. (2006). Confirming proteinprotein interactions by text mining,”
    6. (2005). Corpus design for biomedical natural language processing,”
    7. (1998). Dependency-based evaluation of MINIPAR,”
    8. (2005). Empirical data on corpus design and usage in biomedical natural language processing,”
    9. (2004). Extracting human protein interactions from MEDLINE using a full-sentence parser,”
    10. (2005). Extracting relations with integrated information using kernel methods,”
    11. (2005). From protein networks to biological systems,”
    12. (2005). G r e e n w o o d ,M .S t e v e n s o n ,Y .G u o ,H .H a r k e m a ,a n d A. Roberts, “Automatically acquiring a linguistically motivatedgenicinteractionextractionsystem,”inProceedingsofthe
    13. (2005). Genic interaction extraction with semantic and syntactic chains,”
    14. (2005). J.Saric,L.Jensen,R.Ouzounova,I.Rojas,andP.Bork,“Largescale extraction of protein/gene relations for model organisms,”
    15. (2005). Learning genic interactions without expert domain knowledge: comparison of different ILP algorithms,”
    16. (2005). Learning to extract
    17. (2005). LLL05 challenge: genic interaction extractionidentification of language patterns based on alignment and fi-nite state automata,”
    18. (2006). M.Zhang,J.Zhang,J.Su,andG.Zhou,“Acompositekernelto extractrelationsbetween entitieswithbothflatandstructured features,”
    19. (2007). Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach,”
    20. (2005). Protein-protein interaction extraction: a supervised learning approach,”
    21. (2002). S t a p l e y ,L .A .K e l l e y ,a n dM .J .S t e r n b e r g ,“ P r e d i c t i n g the sub-cellular location of proteins from text using support vector machines,”
    22. (2005). S.Katrenko,M.S.Marshall,M.Roos,andP.Adriaans,“Learning biological interactions from Medline abstracts,”
    23. (2004). u a n g ,X .Z h u ,Y .H a o ,D .G .P a y a n ,K .Q u ,a n dM .L i , “Discovering patterns to extract protein-protein interactions from full texts,”

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.