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Ou-Yang L, Zhang XF, Zhang J, Chen J, Wu M. Editorial: Machine Learning and Mathematical Models for Single-Cell Data Analysis. Front Genet 2022; 13:911999. [PMID: 35719405 PMCID: PMC9204245 DOI: 10.3389/fgene.2022.911999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
- *Correspondence: Le Ou-Yang,
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics and Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Jin Chen
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore
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Wang L, Miao X, Nie R, Zhang Z, Zhang J, Cai J. MultiCapsNet: A General Framework for Data Integration and Interpretable Classification. Front Genet 2021; 12:767602. [PMID: 34899854 PMCID: PMC8652257 DOI: 10.3389/fgene.2021.767602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/25/2021] [Indexed: 12/16/2022] Open
Abstract
The latest progresses of experimental biology have generated a large number of data with different formats and lengths. Deep learning is an ideal tool to deal with complex datasets, but its inherent “black box” nature needs more interpretability. At the same time, traditional interpretable machine learning methods, such as linear regression or random forest, could only deal with numerical features instead of modular features often encountered in the biological field. Here, we present MultiCapsNet (https://github.com/wanglf19/MultiCapsNet), a new deep learning model built on CapsNet and scCapsNet, which possesses the merits such as easy data integration and high model interpretability. To demonstrate the ability of this model as an interpretable classifier to deal with modular inputs, we test MultiCapsNet on three datasets with different data type and application scenarios. Firstly, on the labeled variant call dataset, MultiCapsNet shows a similar classification performance with neural network model, and provides importance scores for data sources directly without an extra importance determination step required by the neural network model. The importance scores generated by these two models are highly correlated. Secondly, on single cell RNA sequence (scRNA-seq) dataset, MultiCapsNet integrates information about protein-protein interaction (PPI), and protein-DNA interaction (PDI). The classification accuracy of MultiCapsNet is comparable to the neural network and random forest model. Meanwhile, MultiCapsNet reveals how each transcription factor (TF) or PPI cluster node contributes to classification of cell type. Thirdly, we made a comparison between MultiCapsNet and SCENIC. The results show several cell type relevant TFs identified by both methods, further proving the validity and interpretability of the MultiCapsNet.
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Affiliation(s)
- Lifei Wang
- Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China.,China National Center for Bioinformation, Beijing, China.,Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xuexia Miao
- China National Center for Bioinformation, Beijing, China.,Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Rui Nie
- China National Center for Bioinformation, Beijing, China.,Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhang Zhang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jiang Zhang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jun Cai
- China National Center for Bioinformation, Beijing, China.,Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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