Extending ontologies by finding siblings using set expansion techniques.

Motivation: Ontologies are an everyday tool in biomedicine to capture and represent knowledge. However, many ontologies lack a high degree of coverage in their domain and need to improve their overall quality and maturity. Automatically extending sets of existing terms will enable ontology engineers to systematically improve text-based ontologies level by level.

A Maximum-Entropy approach for accurate document annotation in the biomedical domain.

The increasing number of scientific literature on the Web and the absence of efficient tools used for classifying and searching the documents are the two most important factors that influence the speed of the search and the quality of the results. Previous studies have shown that the usage of ontologies makes it possible to process document and query information at the semantic level, which greatly improves the search for the relevant information and makes one step further towards the Semantic Web. A fundamental step in these approaches is the annotation of documents with ontology concepts, which can also be seen as a classification task.

Structural modeling of histone methyltransferase complex Set1C from Saccharomyces cerevisiae using constraint-based docking.

Set1C is a histone methyltransferase playing an important role in yeast gene regulation. Modeling the structure of this eight-subunit protein complex is an important open problem to further elucidate its functional mechanism. Recently, there has been progress in modeling of larger complexes using constraints to restrict the combinatorial explosion in binary docking of subunits.

GoWeb: a semantic search engine for the life science web.

Background: Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason.

Triangle network motifs predict complexes by complementing high-error interactomes with structural information.

Background: A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs.

Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy.

Background: Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms.

The stem cell marker prominin-1/CD133 on membrane particles in human cerebrospinal fluid offers novel approaches for studying central nervous system disease.

Cerebrospinal fluid (CSF) is routinely used for diagnosing and monitoring neurological diseases. The CSF proteins used so far for diagnostic purposes (except for those associated with whole cells) are soluble. Here, we show that human CSF contains specific membrane particles that carry prominin-1/CD133, a neural stem cell marker implicated in brain tumors, notably glioblastoma.