Automating terminological networks to link heterogeneous biomedical databases.

As cross-disciplinary research escalates, researchers are facing the challenge of linking disparate biomedical databases that have been developed without common indexes. Manually indexing these large-scale databases is laborious and often impractical. Solutions involving mediating terminologies have been proposed, but coordination of terms from the databases of interest to these mediating terminologies is also laborious, and regular synchronization between indexes is an additional problem. In this study we describe a novel method of linking heterogeneous databases using terminology networks constructed with automated mapping methods. Linkage was established between two disparate biomedical databases (SNOMED-CT and HDG), using two relevant intermediating databases (UMLS and OMIM). One gold standard of 514 distinct matches is used as proof-of-principle. In conclusion, as hypothesized, 1) Manually curated pathways provide high precision, but offer low recall, 2) the automated terminology pathways can significantly increase recall at acceptable precision. Taken together, our conclusion may suggest the combined manual and automated terminology networks could offer recall and precision in an incremental manner

Two biomedical sublanguages: a description based on the theories of Zellig Harris.

Natural language processing (NLP) systems have been developed to provide access to the tremendous body of data and knowledge that is available in the biomedical domain in the form of natural language text. These NLP systems are valuable because they can encode and amass the information in the text so that it can be used by other automated processes to improve patient care and our understanding of disease processes and treatments. Zellig Harris proposed a theory of sublanguage that laid the foundation for natural language processing in specialized domains. He hypothesized that the informational content and structure form a specialized language that can be delineated in the form of a sublanguage grammar. The grammar can then be used by a language processor to capture and encode the salient information and relations in text. In this paper, we briefly summarize his language and sublanguage theories. In addition, we summarize our prior research, which is associated with the sublanguage grammars we developed for two different biomedical domains. These grammars illustrate how Harris' theories provide a basis for the development of language processing systems in the biomedical domain. The two domains and their associated sublanguages discussed are: the clinical domain, where the text consists of patient reports, and the biomolecular domain, where the text consists of complete journal articles.

Of truth and pathways: chasing bits of information through myriads of articles.

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data.

The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for efficient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information.