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XAI-enabled neural network analysis of metabolite spatial distributions

文献类型：期刊论文

作者Ma, Wenwu1,2,6,7,8; Luo, Lanfang1,2,6,7; Liang, Kun1,2,6,7; Liu, Taoyan1,2,6,7; Su, Jiali1,2,6,7; Wang, Yuefan1,2,6,7; Li, Jun3,4,5; Zhou, S. Kevin3,4,5; Shyh-Chang, Ng1,2,6,7
刊名ANALYTICAL AND BIOANALYTICAL CHEMISTRY
出版日期2023-04-21
页码12
ISSN号1618-2642
关键词Mass spectrometry imaging Deep neural networks Feature extraction Pathway analysis Aging
DOI10.1007/s00216-023-04694-8
英文摘要We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods' performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection-based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection-based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy.
资助项目National Key R&D Program of China[2019YFA0801701] ; National Natural Science Foundation of China[91957202] ; CAS Project for Young Scientists in Basic Research[YSBR-012] ; State Key Laboratory of Stem Cell and Reproductive Biology
WOS研究方向Biochemistry & Molecular Biology ; Chemistry
语种英语
出版者SPRINGER HEIDELBERG
WOS记录号WOS:000976256000003
源URL[http://119.78.100.204/handle/2XEOYT63/21409]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shyh-Chang, Ng
作者单位1.Chinese Acad Sci, Inst Stem Cell & Regenerat, Beijing, Peoples R China
2.Beijing Inst Stem Cell & Regenerat Med, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc Chinese Acad Sci, Beijing, Peoples R China
4.Univ Sci & Technol China, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Sch Biomed Engn, Suzhou, Peoples R China
5.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou, Peoples R China
6.Chinese Acad Sci, State Key Lab Stem Cell & Reprod Biol, Beijing, Peoples R China
7.Univ Chinese Acad Sci, Beijing, Peoples R China
8.Univ Sci & Technol China, Dept Life Sci & Med, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Wenwu,Luo, Lanfang,Liang, Kun,et al. XAI-enabled neural network analysis of metabolite spatial distributions[J]. ANALYTICAL AND BIOANALYTICAL CHEMISTRY,2023:12.
APA Ma, Wenwu.,Luo, Lanfang.,Liang, Kun.,Liu, Taoyan.,Su, Jiali.,...&Shyh-Chang, Ng.(2023).XAI-enabled neural network analysis of metabolite spatial distributions.ANALYTICAL AND BIOANALYTICAL CHEMISTRY,12.
MLA Ma, Wenwu,et al."XAI-enabled neural network analysis of metabolite spatial distributions".ANALYTICAL AND BIOANALYTICAL CHEMISTRY (2023):12.

入库方式： OAI收割

来源：计算技术研究所

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