A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways
文献类型：期刊论文
| 作者 | Hu, Jili2,5; Yu, Weiqiang1,3,4; Dai, Yuting1,4; Liu, Can2; Wang, Yongkang2,5; Wu, Qingfa1,3,4 |
| 刊名 | JOURNAL OF ONCOLOGY
 |
| 出版日期 | 2022-09-09 |
| 卷号 | 2022 |
| ISSN号 | 1687-8450 |
| DOI | 10.1155/2022/2965166 |
| 通讯作者 | Wu, Qingfa(wuqf@ustc.edu.cn) |
| 英文摘要 | Background. Gastric cancer (GC) is one of the deadliest cancers in the world, with a 5-year overall survival rate of lower than 20% for patients with advanced GC. Genomic information is now frequently employed for precision cancer treatment due to the rapid advancements of high-throughput sequencing technologies. As a result, integrating multiomics data to construct predictive models for the GC patient prognosis is critical for tailored medical care. Results. In this study, we integrated multiomics data to design a biological pathway-based gastric cancer sparse deep neural network (GCS-Net) by modifying the P-NET model for long-term survival prediction of GC. The GCS-Net showed higher accuracy (accuracy = 0.844), area under the curve (AUC = 0.807), and F1 score (F1 = 0.913) than traditional machine learning models. Furthermore, the GCS-Net not only enables accurate patient survival prognosis but also provides model interpretability capabilities lacking in most traditional deep neural networks to describe the complex biological process of prognosis. The GCS-Net suggested the importance of genes (UBE2C, JAK2, RAD21, CEP250, NUP210, PTPN1, CDC27, NINL, NUP188, and PLK4) and biological pathways (Mitotic Anaphase, Resolution of Sister Chromatid Cohesion, and SUMO E3 ligases) to GC, which is consistent with the results revealed in biological- and medical-related studies of GC. Conclusion. The GCS-Net is an interpretable deep neural network built using biological pathway information whose structure represents a nonlinear hierarchical representation of genes and biological pathways. It can not only accurately predict the prognosis of GC patients but also suggest the importance of genes and biological pathways. The GCS-Net opens up new avenues for biological research and could be adapted for other cancer prediction and discovery activities as well. |
| WOS关键词 | OVEREXPRESSION ; ASSOCIATION ; ARREST ; GROWTH ; MODEL |
| 资助项目 | University Excellent Talent Funding Project of Anhui Province ; Natural Science Project of Anhui University of Chinese Medicine ; Industry-University Cooperation Collaborative Education Project of Ministry of Education of the People's Republic of China ; [gxgnfx2020088] ; [2020wtzx02] ; [202101123001] |
| WOS研究方向 | Oncology |
| 语种 | 英语 |
| 出版者 | HINDAWI LTD |
| WOS记录号 | WOS:000860646800002 |
| 资助机构 | University Excellent Talent Funding Project of Anhui Province ; Natural Science Project of Anhui University of Chinese Medicine ; Industry-University Cooperation Collaborative Education Project of Ministry of Education of the People's Republic of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/129141]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wu, Qingfa |
| 作者单位 | 1.Univ Sci & Technol China, CAS Key Lab Innate Immun & Chron Dis, Hefei, Anhui, Peoples R China 2.Anhui Univ Chinese Med, Sch Med Informat Engn, Hefei, Anhui, Peoples R China 3.Univ Chinese Acad Sci, Inst Basic Med & Canc, Chinese Acad Sci, BGI Ctr,IBMC,Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China 4.Univ Sci & Technol China, Sch Life Sci, Hefei, Anhui, Peoples R China 5.China Acad Chinese Med Sci, Anhui Comp Applicat Res Inst Chinese Med, Hefei, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Jili,Yu, Weiqiang,Dai, Yuting,et al. A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways[J]. JOURNAL OF ONCOLOGY,2022,2022. |
| APA | Hu, Jili,Yu, Weiqiang,Dai, Yuting,Liu, Can,Wang, Yongkang,&Wu, Qingfa.(2022).A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways.JOURNAL OF ONCOLOGY,2022. |
| MLA | Hu, Jili,et al."A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways".JOURNAL OF ONCOLOGY 2022(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。