CBioC: beyond a prototype for collaborative annotation of molecular interactions from the literature.

In molecular biology research, looking for information on a particular entity such as a gene or a protein may lead to thousands of articles, making it impossible for a researcher to individually read these articles and even just their abstracts. Thus, there is a need to curate the literature to get various nuggets of knowledge, such as an interaction between two proteins, and store them in a database. However the body of existing biomedical articles is growing at a very fast rate, making it impossible to curate them manually. An alternative approach of using computers for automatic extraction has problem with accuracy. We propose to leverage the advantages of both techniques, extracting binary relationships between biological entities automatically from the biomedical literature and providing a platform that allows community collaboration in the annotation of the extracted relationships. Thus, the community of researchers that writes and reads the biomedical texts can use the server for searching our database of extracted facts, and as an easy-to-use web platform to annotate facts relevant to them. We presented a preliminary prototype as a proof of concept earlier(1). This paper presents the working implementation available for download at http://www.cbioc.org as a browser-plug in for both Internet Explorer and FireFox. This current version has been available since June of 2006, and has over 160 registered users from around the world. Aside from its use as an annotation tool, data from CBioC has also been used in computational methods with encouraging results.

Understanding the global properties of functionally-related gene networks using the gene ontology.

The global behavior of interactions between genes can be investigated by forming the network of functionally-related genes using the annotations based on the Gene Ontology. We define two genes to be connected when the pair of genes is involved in the same biological process. There has been other work on the analysis of different kinds of cellular and metabolic networks, such as gene coexpression network, in which genes are paired when they are found to be coexpressed in the microarray experiments. We observe that our functionally-related gene networks among humans, fruit flies, worms and yeast exhibit the small-world property, but all except the network of worms show the existence of the scale-free property.

A knowledge based approach for representing and reasoning about signaling networks.

MOTIVATION: In this paper we propose to use recent developments in knowledge representation languages and reasoning methodologies for representing and reasoning about signaling networks. Our approach is different from most other qualitative systems biology approaches in that it is based on reasoning (or inferencing) rather than simulation. Some of the advantages of our approach are, we can use recent advances in reasoning with incomplete and partial information to deal with gaps in signal network knowledge; and can perform various kinds of reasoning such as planning, hypothetical reasoning and explaining observations. RESULTS: Using our approach we have developed the system BioSigNet-RR for representation and reasoning about signaling networks. We use a NFkappaB related signaling pathway to illustrate the kinds of reasoning and representation that our system can currently do. AVAILABILITY: The system is available on the Web at http://www.public.asu.edu/~cbaral/biosignet