Research Publications

Now showing 1 - 3 of 3

Open Access
Identifying Ordinal Nature Inherited Proteins Associated with a Certain Disease
(Faculty of Humanities and Sciences, SLIIT, 2022-10-07) Samarawickrama, O.; Jayatillake, R.; Amaratunga, D.
Proteomic studies are studies of protein expression levels. They are growing swiftly with the steady improvement in technology and knowledge of cell biology. 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 treatments. This research involves analyzing data from an early-stage study of which the main purpose was to identify differentially expressed proteins. There are three progressively serious disease states (healthy to mild to severe) in this study. The analysis can be categorized into 2 stages as univariate and multi-protein analysis. The approach of the univariate analysis was to implement continuation ratio modeling considering one protein at a time to pick those that exhibit potential ordinality. Penalized continuation ratio modeling using lasso regularization incorporated with bootstrapping proteins was performed as the next stage to identify protein combinations that perform well together. Combining results of the univariate and multi-protein analyses identified 20 proteins that join forces to discriminate disease severity with an ordinal setting and 21 proteins that are effective each on its own.
Open Access
Testing For Group Differences in Proteomics Data with Left Censored Data and a Limited Sample Size
(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Anurangi, P.A.L.A; Amaratunga, D.; Viswakula, S.D.
This research study aims to assess how a specific treatment influences the levels of three proteins when left-censored observations are present in a limited sample size. The dataset contained paired data gathered from 20 subjects categorized into 4 groups with increasing dosages, collected before and after administrating the treatment. The primary objective of this study is to evaluate whether there is an increase in response with increasing dosage for each of the proteins. To check the adherence of data to standard distribution, Cumulative Distribution Function (CDF) plots were used. To obtain summary statistics, Regression on Order Statistics (ROS), Maximum Likelihood Estimate (MLE) and Kaplan-Meier (KM) methods were utilized. ROS assumed to be the estimate that generally works well for the dataset as KM was unable to estimate the median for highly censored data and MLE produced unrealistic values for mean in some cases. Various matched paired tests were used to assess differences between before treatment and after treatment. The censored sign test, censored sign rank test, Peto Prentice test, and censored paired test all produced consistent conclusions across different alternative hypotheses, confirming higher protein concentrations after treatment. To evaluate mean differences, censored ANOVA, permutation tests, Peto Peto test, and Kruskal Wallis test were employed. No method demonstrated clear superiority over others. Jonckheere Terpstra test revealed the presence of group trend across increasing dosages. Multiple detection limits did not significantly impact the conclusions drawn from the study, and their consideration did not pose additional burdens. In conclusion, the treatment had a significant effect on protein levels, with dose variations influencing the outcome.
Embargo
Identifying Proteins Associated with Disease Severity
(Faculty of Humanities and Sciences, SLIIT, 2022-09-15) Samarawickrama, O; Jayatillake, R; Amaratunga, D
Proteomic 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.

Research Publications

Browse

Filters

Advanced Search

Filter by

Settings

Sort By

Results per page

Search Results