Novel stabilized models to characterize gene-gene interactions by utilizing transcriptome data

Kossinna, Thalagala Kossinnage Pathum Subhashana

Novel stabilized models to characterize gene-gene interactions by utilizing transcriptome data

dc.contributor.advisor	Long, Quan
dc.contributor.advisor	Zhang, Qingrun
dc.contributor.author	Kossinna, Thalagala Kossinnage Pathum Subhashana
dc.contributor.committeemember	Arnold, Paul Daniel
dc.contributor.committeemember	De Leon, Alexander
dc.date	2022-11
dc.date.accessioned	2022-10-03T19:16:44Z
dc.date.available	2022-10-03T19:16:44Z
dc.date.issued	2022-09-28
dc.description.abstract	Machine learning models employed in genetics often grapple with issues related to the "curse of dimensionality". Furthermore, due to the inherent noisy nature of most -omics data, most methods suffer from the problem of "stability": i.e., even slight perturbations of the original data may result in wholly different outcomes. This becomes particularly true when dealing with interactions as the number of potential interactions are usually astronomical. In this thesis, we present two novel methods: 1) Stabilized COre gene and Pathway Election (SCOPE) and 2) Interaction Bridged Association Study (IBAS) that uses two differing approaches in analyzing biological interactions. SCOPE employs a stabilized form of the LASSO that is better able to handle highly correlated expression data and a co-expression network analysis that identifies "core" genes that may be of interest as well as the underlying biological pathways or mechanisms by which they interact. Stabilizing these results across six cancers of The Cancer Genome Atlas uncovered hallmark cancer pathways as well as a novel potential therapeutic target of kidney cancer, CD63. IBAS utilizes a "data-bridge" composed of dimensionality reduced pathway level interactions of the transcriptome to identify genes associated with a phenotype of interest using the Sequence Kernel Association Test (SKAT), in a disentangled form of the Transcriptome Wide Association Study. Application to the Wellcome Trust Case Control Consortium reveals novel gene candidates with literature reviews highlighting their potential for further study. In conclusion, we have developed two novel methodologies in analyzing complex interaction patterns in -omics data using stabilized machine learning methods, paving the way to further understand the biological interactions underlying complex disease.	en_US
dc.identifier.citation	Kossinna, T. K. P. S. (2022). Novel stabilized models to characterize gene-gene interactions by utilizing transcriptome data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.uri	http://hdl.handle.net/1880/115339
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40339
dc.language.iso	eng	en_US
dc.publisher.faculty	Cumming School of Medicine	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	Machine Learning	en_US
dc.subject	Pan-cancer Analysis	en_US
dc.subject	TCGA	en_US
dc.subject	CD63	en_US
dc.subject	SCOPE	en_US
dc.subject	IBAS	en_US
dc.subject	Stabilized Methods	en_US
dc.subject	Biological Interactions	en_US
dc.subject	data-bridge	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.classification	Statistics	en_US
dc.title	Novel stabilized models to characterize gene-gene interactions by utilizing transcriptome data	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Medicine – Biochemistry and Molecular Biology	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