[1]高冲,秦玉芳,陈明.基于通路活性的抗癌药物敏感性预测[J].中国医学物理学杂志,2021,38(12):1569-1574.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.020]
　GAO Chong,QIN Yufang,et al.Predicting anticancer drug sensitivity based on pathway activity[J].Chinese Journal of Medical Physics,2021,38(12):1569-1574.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.020]
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基于通路活性的抗癌药物敏感性预测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第12期
页码:
1569-1574
栏目:
其他（激光医学等）
出版日期:
2021-12-24

文章信息/Info

Title:
Predicting anticancer drug sensitivity based on pathway activity
文章编号:
1005-202X（2021）12-1569-06
作者:
高冲1; 2; 秦玉芳1; 2; 陈明1; 2
1.上海海洋大学信息学院， 上海 201306； 2.农业农村部渔业信息重点实验室， 上海 201306
Author(s):
GAO Chong1;  2;  QIN Yufang1;  2;  CHEN Ming1;  2
1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China 2. Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs of the Peoples Republic of China, Shanghai 201306, China
关键词:
癌症; 药物敏感性; 通路活性; 基因表达; 个性化医疗
Keywords:
cancer drug sensitivity pathway activity gene expression personalized medicine
分类号:
R318；TP181
DOI:
DOI:10.3969/j.issn.1005-202X.2021.12.020
文献标志码:
A
摘要:
通路数据库是系统分析基因功能，联系基因组信息和功能信息的知识库，通路作为基因功能集合能够提高预测模型的预测能力和解释能力。本研究通过整合KEGG通路数据和CCLE基因表达谱推断通路活性并结合药物数据建立药物敏感性预测模型。在推断通路活性时，并没有把通路简单地作为基因集合，而是选择通路中的高连接度基因，取高连接度基因的平均表达水平作为通路活性值，合并每个通路的活性向量得到通路活性矩阵，然后输入到弹性网进行药物敏感性预测。实验结果表明，对于大多数药物，使用基于通路中高连接度基因的计算分析方法更有利于药物敏感性预测，同时能识别出更多与药物相关基因的通路。
Abstract:
Pathway database is a knowledge base for analyzing gene functions systematically and linking genomic information and functional information. As a collection of gene functions, pathways can improve the prediction ability and interpretation ability of prediction models. Herein the pathway activity is inferred by the integration of KEGG pathway data and CCLE gene expression profile, and then is combined with drug data to establish a drug sensitivity prediction model. When inferring the activity of the pathway, the pathway is not simply treated as a collection of genes. The genes with high connectivity in the pathway are selected in the study, and the average expression level of the genes with high connectivity is taken as the pathway activity value. The pathway activity matrix which is obtained by combining the activity vector of each pathway is input to the elastic net for drug sensitivity prediction. Experimental results show that for most drugs, the use of computational analysis methods based on the genes with high connectivity in pathways is more conducive to drug sensitivity prediction, and at the same time it can identify more pathways for drug-related genes.

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备注/Memo

备注/Memo:
【收稿日期】2021-06-25 【基金项目】国家自然科学基金（61702325）；国家重点研发计划项目（2018YFD0701003）；上海市科技创新计划项目（20dz1203800） 【作者简介】高冲，硕士在读，研究方向：机器学习、生物信息，E-mail: gaochongc@sina.com 【通信作者】秦玉芳，博士，副教授，研究方向：机器学习、生物信息，E-mail: yfqin@shou.edu.cn
更新日期/Last Update: 2021-12-24