- Poster presentation
- Open Access
BioLMiner and the BioCreative II.5 challenge
© Manderick et al; licensee BioMed Central Ltd. 2010
- Published: 06 October 2010
- Conditional Random Field
- Domain Specific Knowledge
- Feature Fuse
- Sequence Label
- Gene Mention
The input data are the original articles from biological literature databases like MEDLINE [http://medline.cos.com/] or journals like FEBS letters [http://www.elsevier.com/locate/febslet/]. The output data are the annotated articles together with the information extracted. Some existing gene and protein databases and biological resources are used as external background knowledge, like Entrez Gene [http://jura.wi.mit.edu/entrez_gene/], UniProt [http://www.uniprot.org], MINT [http://mint.bio.uniroma2.it], IntAct [http://www.ebi.ac.uk/intact] and BioThesaurus [http://pir.georgetown.edu/iprolink/biothesaurus] .
The core components of BioLMiner are
the Gene Mention Recognizer (GMRer)
the Gene Normalizer (GNer)
the Interaction Article Classifier (IACer)
the Protein-Protein Interaction Pair Extractor (PPIEor)
Two machine learning techniques are used to develop the four components, including Support Vector Machines (SVMs)  and Conditional Random Fields (CRFs) , to address classification and sequence labeling problems. For GMRer, a hybrid recognizer is developed based on one sequence labeling model using CRFs and two classification model using SVMs. For GNer, IACer and PPIEor, a binary classifier using SVMs is developed respectively. In order to achieve good performance, our main efforts focus on how to design methods to extract rich and informative features and to combine them effectively. These features fuse the information of the context in the article, domain specific knowledge, the analysis using natural language processing (NLP) tools or specific ones to the biological domain (Bio-NLP). A full description of BioLMiner can be found in [3, 4].
BioLMiner participated in the interaction normalization task (INT) using GNer and interaction pair task (IPT) using PPIEor in the BioCreative II.5 challenge . For the INT, the F β -1 measure was 0.289, which ranked second of the 10 participating teams for this task. For the IPT, the F β -1 measure was 0.252, which ranked first of the 9 participating teams for this task.
The current state of the art performance is far from satisfactory, especially for the IPT. PPI pairs that appear in the figures or tables, span different sentences or interact with themselves cannot be handled well for the moment. More advanced techniques need to be exploited in the future, like anaphora resolution used for semantic analysis to detect the inter-sentence PPI pairs.
- Vapnik V: The nature of statistical learning theory. New York: Springer; 1995.View ArticleGoogle Scholar
- Lafferty J, McCallum A, Pereira F: Conditional random fields: probabilistic models for segmenting and labeling sequence data. Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001) 2001, 282–289.Google Scholar
- Chen Y: Biological Literature Miner: Gene Mention Recognition and Protein-Protein Interaction Pair Extraction. PhD thesis. Vrije Universiteit Brussel 2010.Google Scholar
- Liu F: Biological Literature Miner: Gene Normalization and Interaction Article Classification. PhD thesis. Vrije Universiteit Brussel; 2010.Google Scholar
- Krallinger M, Leitner F, Valencia A: The BioCreative II.5 challenge overview. Proceedings of BioCreative II.5 Workshop 2009, 19.Google Scholar
This article is published under license to BioMed Central Ltd.