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Abstract. Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the BackPropagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, andmost importantly -prevention from over-fitting for prediction of peptide binding to HLA.

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The inevitable move from a single large scale server to a distributed Grid environment is beginning to be realized across international Grid test-bed like Pacific Rim Applications and Grid Middleware Assembly (PRAGMA). Although jobs submitted to a single large server have been widely analyzed, job characteristics in a Grid environment are different as we found in our analysis of jobs submitted in the PRAGMA Grid test-bed. This paper reports on the analysis of jobs submitted across the PRAGMA Grid test-bed. The job types are categorized and the runtime of jobs is captured, using the Multi-Organization Grid Accounting System (MOGAS), and analyzed.The number of jobs submitted across organizations, indicating the level of resource sharing among participants, is also captured by the system.

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