MultiCapsNet: A General Framework for Data Integration and Interpretable Classification.

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 te

SUBMITTER: Wang L 

PROVIDER: S-EPMC8652257 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature