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Classifying abstracts of biomedical literature for stem cell research

Stem cells [12, 13] are biological cells that can differentiate into more specialized cells and have the ability to produce more stem cells through mitosis. It is widely believed, that if they are better understood, stem cell therapies can be developed, which would allow a dramatic improvement of the treatment of human diseases like cancer, Parkinson’s, cardiac failures and many more. Due to their self-renewing and specializing property along with the widespread optimism among the biomedical research community, many scientific research papers on this topic are being published in biomedical journals. The U.S. National Library of medicine provides the MEDLINE/PubMed1 database, which contains references to biomedical articles. It accommodates over 19 million references to articles that are published in over 5,5000 journals with biomedical focus, which makes it the largest bibliographic database for biomedical literature. Because of its sheer size and the not necessarily consistent terminology in biomedical sciences looking for bibliographical references only with keyword-based queries can be a very time consuming activity. In order to make this database more accessible for biologists, more sophisticated search methods than the classical keyword-based search through title and abstract of the articles have been developed. We briefly mention three approaches to facilitate the life of the researcher: searching with MeSH terms and the two projects GoPubMed and Caipirini. To deal with the problem of ambiguous vocabulary in biomedical sciences, MeSH terms2 (Medical Subject Headings Terms) have been introduced. They consist of a hierarchically organized, expert-regulated vocabulary of biomedical terms. Thus, we find for example in the hierarchical level, defined by the path ‘/anatomy/cells/stem cells’, terms like ‘adult stem cells’, ‘embryonic stem cells’, ‘fetal stem cells’, etc. Each newly added bibliographical item in MEDLINE/PubMed can be associated with a set of major and minor MeSH terms, hence allowing to perform a search with a uniform vocabulary. GoPubMed [4] is another project with the aim of simplifying the work of the researcher. It allows the user to narrow the result of his MEDLINE/PubMed query with the help of four categories: the ‘who’, the ‘where’ and the ‘when’ categories let the user specify authors, location and date of publication of his desired references, whereas under the ‘what’ category the user finds the results of his query grouped according to the hierarchies of MeSH and GO (Gene Ontology, another controlled vocabulary for genes), which enables further curtailing of the result by deselecting unwanted terms. However, not all of the articles, that are published in journals are necessarily interesting for biologists. Of the 104,332 articles in MEDLINE/PubMed which are associated with the MeSH-Term ‘stem cell’, we also find papers, that are dealing with funding, business opportunities, patents, moral aspects of stem cell research, legal situation in different countries, etc. MeSH terms do not help to filter these articles out, so it is desirable to have a tool which classifies articles into relevant and non-relevant for stem cell research. The Caipirini [11] project goes away from rigid and often too coarse-grained hierarchies and tries to adopt itself dynamically according to the researcher’s need. It does so by letting the user give examples of relevant and irrelevant documents via PubMed-IDs. Having the samples provided, a support vector regularization model is trained and a third list of PubMed-IDs can be sorted by their estimated probability to belong to the relevant set. The project, though, is optimized for throughput and response time rather than for classification accuracy. The purpose of this student research project is to check the performance of a text classifier for the cognition of biomedical literature on human embryonic stem cells. Therefore, a classifier will be developed using state of the art natural language processing and machine learning techniques, that attempts to classify articles by analyzing abstracts of articles of biomedical publica-