Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information by Sun Kim, Won Kim, Chih-Hsuan Wei, Zhiyong Lu and W. John Wilbur.
Abstract:
The Comparative Toxicogenomics Database (CTD) contains manually curated literature that describes chemical–gene interactions, chemical–disease relationships and gene–disease relationships. Finding articles containing this information is the first and an important step to assist manual curation efficiency. However, the complex nature of named entities and their relationships make it challenging to choose relevant articles. In this article, we introduce a machine learning framework for prioritizing CTD-relevant articles based on our prior system for the protein–protein interaction article classification task in BioCreative III. To address new challenges in the CTD task, we explore a new entity identification method for genes, chemicals and diseases. In addition, latent topics are analyzed and used as a feature type to overcome the small size of the training set. Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants. Integrated with PubTator, a Web interface for annotating biomedical literature, the proposed system also received a positive review from the CTD curation team.
An interesting summary of entity recognition issues in bioinformatics occurs in this article:
The second problem is that chemical and disease mentions should be identified along with gene mentions. Named entity recognition (NER) has been a main research topic for a long time in the biomedical text-mining community. The common strategy for NER is either to apply certain rules based on dictionaries and natural language processing techniques (5–7) or to apply machine learning approaches such as support vector machines (SVMs) and conditional random fields (8–10). However, most NER systems are class specific, i.e. they are designed to find only objects of one particular class or set of classes (11). This is natural because chemical, gene and disease names have specialized terminologies and complex naming conventions. In particular, gene names are difficult to detect because of synonyms, homonyms, abbreviations and ambiguities (12,13). Moreover, there are no specific rules of how to name a gene that are actually followed in practice (14). Chemicals have systematic naming conventions, but finding chemical names from text is still not easy because there are various ways to express chemicals (15,16). For example, they can be mentioned as IUPAC names, brand names, generic names or even molecular formulas. However, disease names in literature are more standardized (17) compared with gene and chemical names. Hence, using terminological resources such as Medical Subject Headings (MeSH) and Unified Medical Language System (UMLS) Metathesaurus help boost the identification performance (17,18). But, a major drawback of identifying disease names from text is that they often use general English terms.
Having a common representative for a group of identifiers for a single entity, should simplify the creation of mappings between entities.
Yes?
[…] this post sounds hauntingly familiar, you may be thinking about Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic informati…, which was the first place finisher at BioCreative 2012 with a mean average precision (MAP) score […]
Pingback by CoIN: a network analysis for document triage « Another Word For It — November 13, 2013 @ 7:16 pm