Network-based screening for ultra-high dimensional survival data subject to semi-competing risks
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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156912 |
| _version_ | 1811689884113436672 |
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| author | Chin, Nicholas Wei Lun |
| author2 | Xiang Liming |
| author_facet | Xiang Liming Chin, Nicholas Wei Lun |
| author_sort | Chin, Nicholas Wei Lun |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:55:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156912 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:55:11Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1569122023-02-28T23:15:36Z Network-based screening for ultra-high dimensional survival data subject to semi-competing risks Chin, Nicholas Wei Lun Xiang Liming School of Physical and Mathematical Sciences LMXiang@ntu.edu.sg Science::Mathematics 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. Bachelor of Science in Mathematical Sciences 2022-04-27T07:36:04Z 2022-04-27T07:36:04Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/156912 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics Chin, Nicholas Wei Lun Network-based screening for ultra-high dimensional survival data subject to semi-competing risks |
| title | Network-based screening for ultra-high dimensional survival data subject to semi-competing risks |
| title_full | Network-based screening for ultra-high dimensional survival data subject to semi-competing risks |
| title_fullStr | Network-based screening for ultra-high dimensional survival data subject to semi-competing risks |
| title_full_unstemmed | Network-based screening for ultra-high dimensional survival data subject to semi-competing risks |
| title_short | Network-based screening for ultra-high dimensional survival data subject to semi-competing risks |
| title_sort | network based screening for ultra high dimensional survival data subject to semi competing risks |
| topic | Science::Mathematics |
| url | https://hdl.handle.net/10356/156912 |
| work_keys_str_mv | AT chinnicholasweilun networkbasedscreeningforultrahighdimensionalsurvivaldatasubjecttosemicompetingrisks |