Biomedical subjects

Dimitar Hristovski

Publications and source records attributed to Dimitar Hristovski.

5 recordsLinked to original sources

PubMed2005

Using literature-based discovery to identify disease candidate genes.

We present BITOLA, an interactive literature-based biomedical discovery support system. The goal of this system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database MEDLINE. To make the system more suitable for disease candidate gene discovery and to decrease the number of candidate relations, we integrate background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as LocusLink and Human Genome Organization (HUGO). BITOLA can also be used as an alternative way of searching the MEDLINE database. The system is available at http://www.mf.uni-lj.si/bitola/.

Algorithms↗

PubMed2004

Distilling conceptual connections from MeSH co-occurrences.

Our aim is to contribute to biomedical text extraction and mining research. In this paper we present exploratory research on the MeSH terms assigned to MEDLINE citations. We analyze MeSH based co-occurrences and identify the interesting ones, i.e., those that are likely to be semantically meaningful. For each selected co-occurring pair we derive a weighted vector representation that emphasizes the verb based functional aspects of the underlying semantics. Preliminary experiments exploring the potential value of these vectors gave us very good results. The larger goal of this project is to contribute to knowledge discovery research by mining the knowledge that is latent within the biomedical literature. It is also to provide a method capable of suggesting cross-disciplinary connections via the pairs derived from all of MEDLINE.

Information Storage and Retrieval↗

PubMed2003

Improving literature based discovery support by genetic knowledge integration.

We present an interactive literature based biomedical discovery support system (BITOLA). The goal of the system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database Medline. To make the system more suitable for disease candidate gene discovery and to decrease the number of candidate relations, we integrate background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as LocusLink, HUGO and OMIM. The BITOLA system can be also used as an alternative way of searching the Medline database. The system is available at http://www.mf.uni-lj.si/bitola/.

Algorithms↗

PubMed2003

Semantic relations asserting the etiology of genetic diseases.

Considerable research is being directed at extracting molecular biology information from text. Particularly challenging in this regard is to identify relations between entities, such as protein-protein interactions or molecular pathways. In this paper we present a natural language processing method for extracting causal relations between genetic phenomena and diseases. After presenting the results of preliminary evaluation, we suggest the use of a graphical display application for viewing the semantic predications produced by the system.

Computer Graphics↗

PubMed2002

Users' information-seeking behavior on a medical library Website.

The Central Medical Library (CMK) at the Faculty of Medicine, University of Ljubljana, Slovenia, started to build a library Website that included a guide to library services and resources in 1997. The evaluation of Website usage plays an important role in its maintenance and development. Analyzing and exploring regularities in the visitors' behavior can be used to enhance the quality and facilitate delivery of information services, identify visitors' interests, and improve the server's performance. The analysis of the CMK Website users' navigational behavior was carried out by analyzing the Web server log files. These files contained information on all user accesses to the Website and provided a great opportunity to learn more about the behavior of visitors to the Website. The majority of the available tools for Web log file analysis provide a predefined set of reports showing the access count and the transferred bytes grouped along several dimensions. In addition to the reports mentioned above, the authors wanted to be able to perform interactive exploration and ad hoc analysis and discover trends in a user-friendly way. Because of that, we developed our own solution for exploring and analyzing the Web logs based on data warehousing and online analytical processing technologies. The analytical solution we developed proved successful, so it may find further application in the field of Web log file analysis. We will apply the findings of the analysis to restructuring the CMK Website.

Consumer Behavior↗