WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores
Genetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/44203/ |
| _version_ | 1848827314757959680 |
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| author | Ren, Yanjie Azlan, Mohd Zain Zhang, Yan Rozita, Abdul Jalil Mahadi, Bahari Norfadzlan, Yusup Mazlina, Abdul Majid Azurah, A. Samah Prasetya, Didik Dwi Nurhafizah Moziyana, Mohd Yusop |
| author_facet | Ren, Yanjie Azlan, Mohd Zain Zhang, Yan Rozita, Abdul Jalil Mahadi, Bahari Norfadzlan, Yusup Mazlina, Abdul Majid Azurah, A. Samah Prasetya, Didik Dwi Nurhafizah Moziyana, Mohd Yusop |
| author_sort | Ren, Yanjie |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Genetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific cancer types, a systematic approach to integrating these mutation data for assessing the impact of gene mutations has been lacking. This study addresses this gap by integrating pan-cancer genomic somatic mutation data and introducing a novel mutation weight fusion (WeiFu) score calculation method. WeiFu computes frequency and weighted fusion scores by cancer type, facilitating the identification of potential driver genes. Evaluation results on an integrated pan-cancer dataset comprising 29 different cancer types demonstrate that WeiFu significantly outperforms current well-known approaches in prediction accuracy, sensitivity, and specificity. Notably, WeiFu recovers 277 known cancer genes among the top 500 ranked candidates and successfully identifies potential driver genes supported by strong evidence. Consequently, WeiFu shows considerable promise for identifying driver genes within the rapidly expanding corpus of cancer genomic data. |
| first_indexed | 2025-11-15T03:58:45Z |
| format | Article |
| id | ump-44203 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:45Z |
| publishDate | 2024 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-442032025-10-06T04:04:59Z https://umpir.ump.edu.my/id/eprint/44203/ WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores Ren, Yanjie Azlan, Mohd Zain Zhang, Yan Rozita, Abdul Jalil Mahadi, Bahari Norfadzlan, Yusup Mazlina, Abdul Majid Azurah, A. Samah Prasetya, Didik Dwi Nurhafizah Moziyana, Mohd Yusop QA75 Electronic computers. Computer science Genetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific cancer types, a systematic approach to integrating these mutation data for assessing the impact of gene mutations has been lacking. This study addresses this gap by integrating pan-cancer genomic somatic mutation data and introducing a novel mutation weight fusion (WeiFu) score calculation method. WeiFu computes frequency and weighted fusion scores by cancer type, facilitating the identification of potential driver genes. Evaluation results on an integrated pan-cancer dataset comprising 29 different cancer types demonstrate that WeiFu significantly outperforms current well-known approaches in prediction accuracy, sensitivity, and specificity. Notably, WeiFu recovers 277 known cancer genes among the top 500 ranked candidates and successfully identifies potential driver genes supported by strong evidence. Consequently, WeiFu shows considerable promise for identifying driver genes within the rapidly expanding corpus of cancer genomic data. IEEE 2024 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/44203/1/WeiFu%20-%20A%20novel%20pan-cancer%20driver%20gene%20identification.pdf Ren, Yanjie and Azlan, Mohd Zain and Zhang, Yan and Rozita, Abdul Jalil and Mahadi, Bahari and Norfadzlan, Yusup and Mazlina, Abdul Majid and Azurah, A. Samah and Prasetya, Didik Dwi and Nurhafizah Moziyana, Mohd Yusop (2024) WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores. IEEE Access, 12. pp. 194762-194773. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2024.3520550 https://doi.org/10.1109/ACCESS.2024.3520550 https://doi.org/10.1109/ACCESS.2024.3520550 |
| spellingShingle | QA75 Electronic computers. Computer science Ren, Yanjie Azlan, Mohd Zain Zhang, Yan Rozita, Abdul Jalil Mahadi, Bahari Norfadzlan, Yusup Mazlina, Abdul Majid Azurah, A. Samah Prasetya, Didik Dwi Nurhafizah Moziyana, Mohd Yusop WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| title | WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| title_full | WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| title_fullStr | WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| title_full_unstemmed | WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| title_short | WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| title_sort | weifu: a novel pan-cancer driver gene identification method using incidence-weighted mutation scores |
| topic | QA75 Electronic computers. Computer science |
| url | https://umpir.ump.edu.my/id/eprint/44203/ https://umpir.ump.edu.my/id/eprint/44203/ https://umpir.ump.edu.my/id/eprint/44203/ |