Explainable Autoencoder Deciphering Key Pathways Underlying Cancer Expression Patterns

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dc.contributor.advisorLiao, Wenyuan
dc.contributor.advisorZhang, Qingrun
dc.contributor.authorYu, Yang
dc.contributor.committeememberThierry Chekouo, Tekougang
dc.contributor.committeememberXuewen, Lu
dc.date2021-11
dc.date.accessioned2021-09-15T16:55:45Z
dc.date.available2021-09-15T16:55:45Z
dc.date.issued2021-09
dc.description.abstractModern machine learning methods have been extensively utilized in gene expression data analysis. In particular, autoencoders (AE) have been employed in processing noisy and heterogenous RNA-Seq data. However, AEs usually lead to “black-box” hidden variables difficult to interpret, hindering downstream experimental validations and clinical translation. To bridge the gap between complicat-ed models and the biological interpretations, we developed a tool, XAE4Exp (eXplainable AutoEn-coder for Expression data), which integrates AE and SHapley Additive exPlanations (SHAP), a flagship technique in the field of eXplainable AI (XAI). It quantitatively evaluates the contributions of each gene to the hidden structure learned by an AE, substantially improving the expandability of AE outcomes. By applying XAE4Exp to The Cancer Genome Atlas (TCGA) breast cancer gene ex-pression data, we revealed intriguing pathways including cell damage management, cell cycle, immune system related pathways underlying breast cancer. This tool will enable researchers and practitioners to analyze high-dimensional expression data intuitively, paving the way towards broad-er uses of deep learning.en_US
dc.identifier.citationYu, Y. (2021). Explainable autoencoder deciphering key pathways underlying cancer expression patterns (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/39202
dc.identifier.urihttp://hdl.handle.net/1880/113876
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity 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.subjectartificial intelligence, machine learning, deep learning, autoencoder, explainable AI, SHAPen_US
dc.subject.classificationEducation--Mathematicsen_US
dc.subject.classificationEducation--Sciencesen_US
dc.subject.classificationBioinformaticsen_US
dc.titleExplainable Autoencoder Deciphering Key Pathways Underlying Cancer Expression Patternsen_US
dc.typemaster thesisen_US
thesis.degree.disciplineMathematics & Statisticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
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