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Biomedical subjects

Rob Jelier

Publications and source records attributed to Rob Jelier.

5 recordsLinked to original sources

CoPub Mapper: mining MEDLINE based on search term co-publication.

BACKGROUND: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. RESULTS: MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. CONCLUSION: The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data.

Algorithms↗

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↗

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↗

Mining microarray datasets aided by knowledge stored in literature.

DNA microarray technology produces large amounts of data. For data mining of these datasets, background information on genes can be helpful. Unfortunately most information is stored in free text. Here, we present an approach to use this information for DNA microarray data mining.

Databases, Genetic↗

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↗