Semantic enabled social-collaborative research framework for proteomics domain

Abstract

The importance of proteomics science is rapidly growing across all areas of biomedical research for the measurement of cellular function. However, the most advanced proteomics technologies, knowledge and datasets have been widely unavailable to the biomedical research communities. These limits make it harder to support the disease and drug related research progress. Moreover, developing common data standards and the validation of deposition is difficult, slow, and an arduous task for any single group. Therefore, proteomics domain necessitates collaboration with diverse researchers for meaningful research progress. The lack of effective collaboration and sharing facilities among researchers with necessary biological expertise is another hurdle for properly designing studies, or developing data standards. The social network centric collaborative research framework could bridge that gap by bringing together expertise in different areas of proteomics, and providing a shared workspace for them by enabling research artifact sharing. Besides, it is a challenging task for a human administrator to manually manage, analyse, and make decisions in a traditional collaborative system. A promising solution to this challenging research problem is the application of the IBMs Autonomic Computing (AC) system. This paper presents a social networking enabled collaborative research framework that allows not only automatically finding relevant researchers, making social connections, co-authorships, but also collaboratively create and share research artifacts, forming a community that could influence research contribution. We also offer semantic richness to the framework by constructing `OntoProteomics' ontology. Finally, we highlight the concept of the AC system that could enable automated functionality in the collaborative environment.