MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. H...

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Published in:Frontiers in genetics Vol. 10; p. 617
Main Authors: Lee, Garam, Kang, Byungkon, Nho, Kwangsik, Sohn, Kyung-Ah, Kim, Dokyoon
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 28.06.2019
Subjects:
Alzheimer’s disease
data integration
gated recurrent unit
Genetics
multimodal deep learning
python package
gated recurrent unit
python package
Alzheimer’s disease
data integration
multimodal deep learning
ISSN:1664-8021, 1664-8021
Online Access:Get full text
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Summary:As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based ult modal ongitudinal ata egration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer's disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset.
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This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics
Edited by: Tao Zeng, Shanghai Institutes for Biological Sciences (CAS), China
Reviewed by: Min Chen, Hunan Institute of Technology, China; Liansheng Wang, Xiamen University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2019.00617