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Most phenotype-identification methods in cell-based screening assume prior knowledge about expected phenotypes or involve intricate parameter-setting. They are useful for analysis targeting known phenotype properties; but need exists to explore, with minimum presumptions, the potentially-interesting phenotypes derivable from data. We present a method for this exploration, using clustering to eliminate phenotype-labelling requirement and GUI visualisation to facilitate parameter-setting. The steps are: outlier-removal, cell clustering and interactive visualisation for phenotypes refinement. For drug-siRNA study, we introduce an auto-merging procedure to reduce phenotype redundancy. We validated the method on two Golgi apparatus screens and showcase its contribution for better understanding of screening-images.

Information Service is a key component of a Grid environment and crucial to the operation of a computational Grid. This paper presents an Information Service for a computational Grid Virtual Organization (VO). The main functionality of this Information Service is the provision of information essential for applications running on a computational Grid. This Information Service is a hierarchical structure that consists of VO layer, site layer, and resource layer. We propose different models of information data organization for Information Service, and experiments were conducted to evaluate the performance of these models. Based on the experimental results, we further introduce the data organization model for our Information Service. The implementation of the Information Service is based on the Globus Toolkit and complies with the OGSI (Open Grid Services Infrastructure) specifications. In this paper, some application experiences of using the Information Service are also presented.