Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3494
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dc.contributor.authorSamarawickrama, O-
dc.contributor.authorJayatillake, R-
dc.contributor.authorAmaratunga, D-
dc.date.accessioned2023-07-30T05:30:52Z-
dc.date.available2023-07-30T05:30:52Z-
dc.date.issued2022-09-15-
dc.identifier.citationO. Samarawickrama, R. Jayatillake and D. Amaratunga (2022). Identifying Proteins Associated with Disease Severity. Proceedings of SLIIT International Conference on Advancements in Sciences and Humanities, (11) October, Colombo, 207 - 211.en_US
dc.identifier.issn2783-8862-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3494-
dc.description.abstractProteomic 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.en_US
dc.language.isoenen_US
dc.publisherFaculty of Humanities and Sciences, SLIITen_US
dc.relation.ispartofseriesPROCEEDINGS OF THE SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES [SICASH];-
dc.subjectProteomic studiesen_US
dc.subjectTrend testsen_US
dc.subjectOrdinal natureen_US
dc.subjectLasso regularizationen_US
dc.subjectBootstrappingen_US
dc.titleIdentifying Proteins Associated with Disease Severityen_US
dc.typeArticleen_US
Appears in Collections:Proceedings of the SLIIT International Conference on Advancements in Sciences and Humanities2022 [SICASH]

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