MOTIVATION: We present a study of antigen expression signals from a newly developed high-throughput protein microarray technique. These signals are a measure of antibody-antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic development. RESULTS: We investigate the characteristics of these expression profiles and show that noise models, normalization, variance estimation and differential expression analysis techniques developed in the context of DNA microarray analysis can be adapted and applied to these protein arrays. Using a high-dimensional dataset containing measurements of expression profiles of antibody reactivity against each protein (295 antigens and 9 controls) in 42 malaria (Plasmodium falciparum) protein arrays derived from 22 donors with various clinical presentations of malaria, we present a methodology for the analysis and identification of significantly expressed antigens targeted by immune responses for individual sera, groups of sera and across stages of infection. We also conduct a short study highlighting the top immunoreactive antigens where we identify three novel high priority antigens for future evaluation. AVAILABILITY: All software programs (in R) used for the analysis described in this paper are freely available for academic purposes at www.igb.uci.edu/servers/servers.html.

In intensive care units, the patients, who are suffering from severe head injuries, usually enter a state of coma. To treat such patients, who are prone to a high risk of mortality, the neurologist adopts certain aggressive and informed decision-making procedures. Designing a decision support system (DSS) that would automate or enhance this kind of treatment procedure is difficult, due to the presence of unclear domain relationships, numerous interacting variables, time criticality and real-time multiple inputs. We illustrate how the decision analysis framework can be exploited to build a consultative DSS for severe head injury management. Specifically, we need to: (a) understand the head injury problem, with its inherent uncertainties, (b) structure the problem, and (c) discern the decision process. The designed system accepts the prognostic factors of a particular patient as input, and subsequently provides treatment advice as output. The effectiveness of the treatment is ranked in terms of patient recovery.