Explainable Autoencoder Deciphering Key Pathways Underlying Cancer Expression Patterns
Date
2021-09
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Abstract
Modern 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.
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Keywords
artificial intelligence, machine learning, deep learning, autoencoder, explainable AI, SHAP
Citation
Yu, 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.