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Biomedical subjects

Saurav Sahay

Publications and source records attributed to Saurav Sahay.

2 recordsLinked to original sources

Discovering biomedical relations utilizing the World-wide Web.

To crate a Semantic Web for Life Sciences discovering relations between biomedical entities is essential. Journals and conference proceedings represent the dominant mechanisms of reporting newly discovered biomedical interactions. The unstructured nature of such publications makes it difficult to utilize data mining or knowledge discovery techniques to automatically incorporate knowledge from these publications into the ontologies. On the other hand, since biomedical information is growing explosively, it is difficult to have human curators manually extract all the information from literature. In this paper we present techniques to automatically discover biomedical relations from the World-wide Web. For this purpose we retrieve relevant information from Web Search engines using various lexico-syntactic patterns as queries. Experiments are presented to show the usefulness of our techniques.

Classification↗

Investigation into biomedical literature classification using support vector machines.

Specific topic search in the PubMed Database, one of the most important information resources for scientific community, presents a big challenge to the users. The researcher typically formulates boolean queries followed by scanning the retrieved records for relevance, which is very time consuming and error prone. We applied Support Vector Machines (SVM) for automatic retrieval of PubMed articles related to Human genome epidemiological research at CDC (Center for disease Control and Prevention). In this paper, we discuss various investigations into biomedical literature classification and analyze the effect of various issues related to the choice of keywords, training sets, kernel functions and parameters for the SVM technique. We report on the various factors above to show that SVM is a viable technique for automatic classification of biomedical literature into topics of interest such as epidemiology, cancer, birth defects etc. In all our experiments, we achieved high values of PPV, sensitivity and specificity.

Abstracting and Indexing↗