Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis
Abstract The prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilan...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.12395 |
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| _version_ | 1841543341502627840 |
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| author | Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan |
| author_facet | Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan |
| author_sort | Lin Zhou |
| collection | DOAJ |
| description | Abstract The prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments. Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). This innovative approach aims to discern various DST tumour types and proficiently discern between early‐late stage tumours, ensuring a high degree of accuracy. The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model. The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early‐late stage DST cases. The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN. |
| format | Article |
| id | doaj-art-d8eb57c5bcd543cfba5412111e0bcfa3 |
| institution | Kabale University |
| issn | 2468-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-d8eb57c5bcd543cfba5412111e0bcfa32025-01-13T14:05:51ZengWileyCAAI Transactions on Intelligence Technology2468-23222024-12-01961572158610.1049/cit2.12395Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosisLin Zhou0Zhengzhi Zhu1Hongbo Gao2Chunyu Wang3Muhammad Attique Khan4Mati Ullah5Siffat Ullah Khan6School of Information Science and Technology University of Science and Technology of China Hefei Anhui ChinaDepartment of Breast Center West District of The Affiliated Hospital of University of Science and Technology of China Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui ChinaSchool of Information Science and Technology University of Science and Technology of China Hefei Anhui ChinaSchool of Biological and Environmental Engineering Chaohu University Chaohu Regional Collaborative Technology Service Center for Rural Revitalization Hefei ChinaDepartment of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al‐Khobar Saudi ArabiaSchool of Information Science and Technology University of Science and Technology of China Hefei Anhui ChinaSchool of Information Science and Technology University of Science and Technology of China Hefei Anhui ChinaAbstract The prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments. Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). This innovative approach aims to discern various DST tumour types and proficiently discern between early‐late stage tumours, ensuring a high degree of accuracy. The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model. The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early‐late stage DST cases. The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.https://doi.org/10.1049/cit2.12395digestive system tumorsearly‐late stagemulti‐omicstumor types |
| spellingShingle | Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis CAAI Transactions on Intelligence Technology digestive system tumors early‐late stage multi‐omics tumor types |
| title | Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis |
| title_full | Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis |
| title_fullStr | Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis |
| title_full_unstemmed | Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis |
| title_short | Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis |
| title_sort | multi omics graph convolutional networks for digestive system tumour classification and early late stage diagnosis |
| topic | digestive system tumors early‐late stage multi‐omics tumor types |
| url | https://doi.org/10.1049/cit2.12395 |
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