Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs
文献类型：期刊论文
| 作者 | Xie, Xinping2; Wang, Fengting2,3; Wang, Guanfu2; Zhu, Weiwei3,4; Du, Xiaodong1; Wang, Hongqiang3,4
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| 刊名 | ARTIFICIAL INTELLIGENCE IN MEDICINE
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| 出版日期 | 2024-06-01 |
| 卷号 | 152 |
| 关键词 | Cancer Drug efficacy prediction Gene expression profiles Gene regulatory networks Precision medicine |
| ISSN号 | 0933-3657 |
| DOI | 10.1016/j.artmed.2024.102864 |
| 通讯作者 | Wang, Hongqiang(hqwang126@126.com) |
| 英文摘要 | Predicting the response of tumor cells to anti-tumor drugs is critical to realizing cancer precision medicine. Currently, most existing methods ignore the regulatory relationships between genes and thus have unsatisfactory predictive performance. In this paper, we propose to predict anti-tumor drug efficacy via learning the activity representation of tumor cells based on a priori knowledge of gene regulation networks (GRNs). Specifically, the method simulates the cellular biosystem by synthesizing a cell-gene activity network and then infers a new lowdimensional activity representation for tumor cells from the raw high-dimensional expression profile. The simulated cell-gene network mainly comprises known gene regulatory networks collected from multiple resources and fuses tumor cells by linking them to hotspot genes that are over- or under-expressed in them. The resulting activity representation could not only reflect the shallow expression profile (hotspot genes) but also mines in-depth information of gene regulation activity in tumor cells before treatment. Finally, we build deep learning models on the activity representation for predicting drug efficacy in tumor cells. Experimental results on the benchmark GDSC dataset demonstrate the superior performance of the proposed method over SOTA methods with the highest AUC of 0.954 in the efficacy label prediction and the best R2 of 0.834 in the regression of half maximal inhibitory concentration (IC50) values, suggesting the potential value of the proposed method in practice. |
| WOS关键词 | SENSITIVITY ; ACCURACY |
| 资助项目 | National Natural Science Foundation of China[61973295] ; National Natural Science Foundation of China[81872276] ; Anhui Province's key Research and Development Project[201904a07020092] ; University Science Research Project of the Educa-tion Department of Anhui Province[KJ2021A0633] ; Laboratory of Operations Research and Data Science of Anhui Jianzhu University[YCSJ2024ZR02] |
| WOS研究方向 | Computer Science ; Engineering ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:001291568500001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Anhui Province's key Research and Development Project ; University Science Research Project of the Educa-tion Department of Anhui Province ; Laboratory of Operations Research and Data Science of Anhui Jianzhu University |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/136006]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wang, Hongqiang |
| 作者单位 | 1.Hefei Univ, Expt Teaching Ctr, Hefei, Peoples R China 2.Anhui Jianzhu Univ, Sch Math & Phys, Hefei, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China 4.Zhongqi AI Lab, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Xinping,Wang, Fengting,Wang, Guanfu,et al. Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2024,152. |
| APA | Xie, Xinping,Wang, Fengting,Wang, Guanfu,Zhu, Weiwei,Du, Xiaodong,&Wang, Hongqiang.(2024).Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs.ARTIFICIAL INTELLIGENCE IN MEDICINE,152. |
| MLA | Xie, Xinping,et al."Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs".ARTIFICIAL INTELLIGENCE IN MEDICINE 152(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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