Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156912
Title: Network-based screening for ultra-high dimensional survival data subject to semi-competing risks
Authors: Chin, Nicholas Wei Lun
Keywords: Science::Mathematics
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chin, N. W. L. (2022). Network-based screening for ultra-high dimensional survival data subject to semi-competing risks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156912
Abstract: As a result of the current proliferation of scientific data of unprecedented magnitude and complexity, ultrahigh dimensional data has become recurrent in a multitude of biological studies. With biomarker identification being a key concern for early disease detection, the ultrahigh dimensionality of data further complicates the complexity of the problem. Feature screening has become increasingly significant in many scientific research but very limited studies consider two types of survival endpoints, consider gene-gene dependencies and ac- count for outliers. In this paper, we enhance joint correlation rank (JCR) screening by utilising Google’s PageRank matrix to incorporate covariate-covariate network information. A nonparanormal approach was also adopted to enable the screening to be more robust to outliers. Through a series of simulations, we highlight its improved performance on identi- fying active covariates accurately. For illustration, the proposed method is applied to colon cancer data, where it is assessed based on prediction performance.
URI: https://hdl.handle.net/10356/156912
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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