Biomedical subjects

Marc Weeber

Publications and source records attributed to Marc Weeber.

5 recordsLinked to original sources

PubMed2004

Contextual annotation of web pages for interactive browsing.

With the information on the World Wide Web and in specialized databases exploding, researchers and physicians are in dire need to browse efficiently though the large corpus of information resources in their field of interest. The focus is not any longer to find everything related to your interest, but it shifts to zooming in, based on context and expanding again in neighboring knowledge domains. This paper describes an attempt to develop a completely new, interactive way of browsing distributed corpora of information without the need for multiple different queries in different information resources. Classical search engines generally treat search requests in isolation. The results for a given query are identical, and do not automatically take on board the context in which the user made the request. The system described here explores implicit contexts as obtained from the document that the user is reading. The new approach merges the searching and browsing into one combined "read-and-search" mode and alleviates the shift users are normally forced to between searching and reading.

Hypermedia↗

PubMed2003

Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide.

The availability of scientific bibliographies through online databases provides a rich source of information for scientists to support their research. However, the risk of this pervasive availability is that an individual researcher may fail to find relevant information that is outside the direct scope of interest. Following Swanson's ABC model of disjoint but complementary structures in the biomedical literature, we have developed a discovery support tool to systematically analyze the scientific literature in order to generate novel and plausible hypotheses. In this case report, we employ the system to find potentially new target diseases for the drug thalidomide. We find solid bibliographic evidence suggesting that thalidomide might be useful for treating acute pancreatitis, chronic hepatitis C, Helicobacter pylori-induced gastritis, and myasthenia gravis. However, experimental and clinical evaluation is needed to validate these hypotheses and to assess the trade-off between therapeutic benefits and toxicities.

Databases, Bibliographic↗

PubMed2003

Ambiguity of human gene symbols in LocusLink and MEDLINE: creating an inventory and a disambiguation test collection.

Genes are discovered almost on a daily basis and new names have to be found. Although there are guidelines for gene nomenclature, the naming process is highly creative. Human genes are often named with a gene symbol and a longer, more descriptive term; the short form is very often an abbreviation of the long form. Abbreviations in biomedical language are highly ambiguous, i.e., one gene symbol often refers to more than one gene. Using an existing abbreviation expansion algorithm,we explore MEDLINE for the use of human gene symbols derived from LocusLink. It turns out that just over 40% of these symbols occur in MEDLINE, however, many of these occurrences are not related to genes. Along the process of making an inventory, a disambiguation test collection is constructed automatically.

Algorithms↗

PubMed2003

Using contextual queries.

Search engines generally treat search requests in isolation. The results for a given query are identical, independent of the user, or the context in which the user made the request. An approach is demonstrated that explores implicit contexts as obtained from a document the user is reading. The approach inserts into an original (web) document functionality to directly activate context driven queries that yield related articles obtained from various information sources.

Databases as Topic↗

PubMed2003

A probabilistic similarity metric for Medline records: a model for author name disambiguation.

We present a model for automatically generating training sets and estimating the probability that a pair of Medline records sharing a last and first name initial are authored by the same individual, based on shared title words, journal name, co-authors, medical subject headings, language, and affiliation, as well as distinctive features of the name itself (i.e., presence of middle initial, suffix, and prevalence in Medline).

Authorship↗