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Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability

文献类型：期刊论文

作者Xi, Jianing4; Wang, Dan3; Yang, Xuebing2; Zhang, Wensheng1,2; Huang, Qinghua4
刊名BIOMEDICAL SIGNAL PROCESSING AND CONTROL
出版日期2023
卷号79页码:9
ISSN号1746-8094
关键词Drug recommendation Explainability Traceability Omic data
DOI10.1016/j.bspc.2022.104144
通讯作者Xi, Jianing(xjn@nwpu.edu.cn) ; Huang, Qinghua(qhhuang@nwpu.edu.cn)
英文摘要The application of Artificial Intelligence (AI) on cancer drug recommendation can prompt the development of personalized cancer therapy. However, most of the current AI drug recommendations cannot give explainable inferences, where their prediction procedures are black boxes, and are difficult to earn the trust of doctors or patients. In explainable inference, the key steps during the recommendation procedures can be located easily, facilitating model adjustment for wrong predictions and model generalization for new drugs/samples. In this paper, we analyze the necessity of developing explainable AI drug recommendation, and propose an evaluation metric called traceability rate. The traceability rate is calculated as the proportion of correct predictions that are traceable along the knowledge graph in all the ground truths. We further conduct an experiment on a benchmark drug response dataset to apply the traceability rate as evaluation metric, where the results show a trade-off between model performance and explainability. Therefore, the explainable AI drug recommendation still demands for further improvement to meet the requirement of clinical personalized therapy.
WOS关键词SENSITIVITY ; INTELLIGENCE
资助项目National Key Research and Development Program of China[2018AAA0102104] ; National Natural Science Foundation of China[61901322] ; National Natural Science Foundation of China[62071382]
WOS研究方向Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000868136200005
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/50284]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Xi, Jianing; Huang, Qinghua
作者单位1.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
4.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China
推荐引用方式
GB/T 7714		 Xi, Jianing,Wang, Dan,Yang, Xuebing,et al. Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2023,79:9.
APA Xi, Jianing,Wang, Dan,Yang, Xuebing,Zhang, Wensheng,&Huang, Qinghua.(2023).Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,79,9.
MLA Xi, Jianing,et al."Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 79(2023):9.

入库方式： OAI收割

来源：自动化研究所

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