Utilizing Machine Learning Methods for Genomic Biomarker Discovery in Prostate and Bladder Cancer
| dc.contributor.advisor | Alhajj, Reda | |
| dc.contributor.author | Liosis, Konstantinos Christos | |
| dc.contributor.committeemember | Rokne, Jon | |
| dc.contributor.committeemember | Bismar, Tarek | |
| dc.contributor.committeemember | Alhajj, Reda | |
| dc.date | 2022-02 | |
| dc.date.accessioned | 2021-12-16T15:42:07Z | |
| dc.date.available | 2021-12-16T15:42:07Z | |
| dc.date.issued | 2021-12-10 | |
| dc.description.abstract | Cancer in all its forms of expression is a major cause of death. In this thesis, prostate and bladder cancer are examined for the purpose of genomic biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either in a very low level based on the genome sequence itself, or in a more abstract way such as measuring the level of gene expression in different disease groups. The latter method is pivotal for this work, since the available datasets consisted of RNA sequencing data, transformed to gene expression levels, as well as a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan-Meier curves, and heatmaps. The experimental work led to the discovery of two gene signatures capable of predicting Therapy Response and Disease Progression with considerable accuracy for bladder cancer patients, the correlation of clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner, and to further exploring and validating the predictive potential of the preexisting HDDA10 genomic signature for prostate cancer patients. | en_US |
| dc.identifier.citation | Liosis, K. C. (2021). Utilizing Machine Learning Methods for Genomic Biomarker Discovery in Prostate and Bladder Cancer (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/39424 | |
| dc.identifier.uri | http://hdl.handle.net/1880/114178 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Science | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject.classification | Bioinformatics | en_US |
| dc.subject.classification | Biostatistics | en_US |
| dc.subject.classification | Computer Science | en_US |
| dc.title | Utilizing Machine Learning Methods for Genomic Biomarker Discovery in Prostate and Bladder Cancer | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |