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

doi:10.1016/j.bspc.2022.104144

	Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability
	Xi, Jianing 1; Wang, Dan2 ; Yang, Xuebing3 ; Zhang, Wensheng3,4 ; Huang, Qinghua 1
Source Publication	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN	1746-8094
	2023
Volume	79 Pages:9
Corresponding Author	Xi, Jianing(xjn@nwpu.edu.cn) ; Huang, Qinghua(qhhuang@nwpu.edu.cn)
Abstract	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.
Keyword	Drug recommendation Explainability Traceability Omic data
DOI	10.1016/j.bspc.2022.104144
WOS Keyword	SENSITIVITY ; INTELLIGENCE
Indexed By	SCI
Language	英语
Funding Project	National Key Research and Development Program of China[2018AAA0102104] ; National Natural Science Foundation of China[61901322] ; National Natural Science Foundation of China[62071382]
Funding Organization	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research Area	Engineering
WOS Subject	Engineering, Biomedical
WOS ID	WOS:000868136200005
Publisher	ELSEVIER SCI LTD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.ia.ac.cn/handle/173211/50285
Collection	精密感知与控制研究中心_人工智能与机器学习
Corresponding Author	Xi, Jianing; Huang, Qinghua
Affiliation	1.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China 2.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
Recommended Citation 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.

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Institution:
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh

精密感知与控制研究中心知识库