Temporal Classifiers for Predicting the Expansion of Medical Subject Headings

Abstract. Ontologies such as the Medical Subject Headings (MeSH) andthe Gene Ontology (GO) play a major role in biology and medicine since they facilitate data integration and the consistent exchange of information between different entities. They can also be used to index and annotate data and literature, thus enabling efficient search and analysis. Unfortunately, maintaining the ontologies manually is a complex, error-prone, and time and personnel-consuming effort. One major problem is the continuous growth of the biomedical literature, which expands by almost 1 million new scientific papers per year, indexed by Medline. The enormous annual increase of scientific publications constitutes the task of monitoring and following the changes and trends in the biomedical domain extremely difficult. For this purpose, approaches that try to learn and maintain ontologies automatically from text and data have been developed in the past. The goal of this paper is to develop temporal classifiers in order to create, for the first time to the best of our knowledge, an automated method that may predict which regions of the MeSH ontology will expand in the near future.

Protein data integration problem

In this chapter, we consider the challenges of information integration in proteomics from the prospective of researchers using information technology as an integral part of their discovery process. Specifically, data integration, meta-data specification, data provenance and data quality, and ontology are discussed here. These are the fundamental problems that need to be solved by the bioinformatics community so that modern information technology can have a deeper impact on the progress of biological discovery