SLIIT International Conference on Advancements in Sciences and Humanities [SICASH] 2022
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Publication Embargo Identifying Proteins Associated with Disease Severity(Faculty of Humanities and Sciences, SLIIT, 2022-09-15) Samarawickrama, O; Jayatillake, R; Amaratunga, DProteomic studies or studies of protein expression levels are growing swiftly with the steady improvement in technology and knowledge on understanding various anomalies affecting humans. Since differentially expressed proteins have an influence on overall cell functionality, this improves discrimination between healthy and diseased states. Identifying prime proteins offers prospective insights for developing optimized and targeted treatment methods. This research involves analyzing data from an early-stage study whose main purpose was to identify differentially expressed proteins. The presence of 3 progressively serious states of disease (healthy to mild to severe) escalates the importance of this study because there is not much research literature that considers ordinal outcomes in studies of this nature. The analysis can be segregated into 2 stages, univariate and multiprotein analysis. Approach of the univariate analysis was to implement continuation ratio model considering one protein at a time to pick those that exhibits potential ordinality. Penalized continuation ratio model using lasso regularization incorporated with bootstrapping proteins was performed as the next stage to identify protein combinations that perform well together. Compound results of the univariate and multi-protein analysis identified 20 most dominant proteins that have the capability to discriminate between the disease states in an ordinal manner satisfactorily.Publication Embargo Reference Ranges and Control Limits that are Resistant to Baseline Outliers(Faculty of Humanities and Sciences, SLIIT, 2022-09-15) Amaratunga, DReference ranges and control limits are used in many settings – for example, to assess a person’s health or to monitor the stability of a manufacturing process. Such ranges are established based on a baseline sample of what is considered normal data, but it is not possible to always avoid a few outliers being present even in this sample. If, as is common, the range is calculated using statistics, such as the mean and standard deviation, which could be influenced by outliers, then the use of such a range could adversely affect the decisions made. This can be avoided by constructing the reference range using statistics that are resistant to outliers. In this paper, we demonstrate the superior performance of such an approach.