Computational approaches for disease gene identification
Identifying disease genes from the human genome is a crucial but challenging task in the area of bioinformatics research and medical health. In wet-lab experiments, disease genes are identified using mutation analysis, which is very expensive and labor intensive. In this thesis, we proposed novel co...
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| Format: | Thesis |
| Language: | English |
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2014
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| Online Access: | https://hdl.handle.net/10356/59238 |
| _version_ | 1826124066735521792 |
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| author | Yang, Peng |
| author2 | Ng See-Kiong |
| author_facet | Ng See-Kiong Yang, Peng |
| author_sort | Yang, Peng |
| collection | NTU |
| description | Identifying disease genes from the human genome is a crucial but challenging task in the area of bioinformatics research and medical health. In wet-lab experiments, disease genes are identified using mutation analysis, which is very expensive and labor intensive. In this thesis, we proposed novel computational approaches to prioritize and identify disease genes. The experimental results show that our work is more robust and accurate than other state-of-the-the-art techniques for disease gene identification. |
| first_indexed | 2024-10-01T06:14:35Z |
| format | Thesis |
| id | ntu-10356/59238 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:14:35Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/592382023-03-04T00:45:54Z Computational approaches for disease gene identification Yang, Peng Ng See-Kiong Kwoh Chee Keong Li Xiaoli School of Computer Engineering Bioinformatics Research Centre DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications Identifying disease genes from the human genome is a crucial but challenging task in the area of bioinformatics research and medical health. In wet-lab experiments, disease genes are identified using mutation analysis, which is very expensive and labor intensive. In this thesis, we proposed novel computational approaches to prioritize and identify disease genes. The experimental results show that our work is more robust and accurate than other state-of-the-the-art techniques for disease gene identification. DOCTOR OF PHILOSOPHY (SCE) 2014-04-28T02:05:13Z 2014-04-28T02:05:13Z 2014 2014 Thesis Yang, P. (2014). Computational approaches for disease gene identification. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/59238 10.32657/10356/59238 en 179 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications Yang, Peng Computational approaches for disease gene identification |
| title | Computational approaches for disease gene identification |
| title_full | Computational approaches for disease gene identification |
| title_fullStr | Computational approaches for disease gene identification |
| title_full_unstemmed | Computational approaches for disease gene identification |
| title_short | Computational approaches for disease gene identification |
| title_sort | computational approaches for disease gene identification |
| topic | DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications |
| url | https://hdl.handle.net/10356/59238 |
| work_keys_str_mv | AT yangpeng computationalapproachesfordiseasegeneidentification |