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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| Veröffentlicht in: | Frontiers in genetics Jg. 10; S. 617 |
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Frontiers Media S.A
28.06.2019
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| Abstract | 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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| AbstractList | 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 multimodal longitudinal data integration 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. 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 multimodal longitudinal data integration 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.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 multimodal longitudinal data integration 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. 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. |
| Author | Nho, Kwangsik Lee, Garam Kang, Byungkon Kim, Dokyoon Sohn, Kyung-Ah |
| AuthorAffiliation | 1 Department of Software and Computer Engineering, Ajou University , Suwon , South Korea 3 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine , Indianapolis, IN , United States 4 Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine , Indianapolis, IN , United States 5 Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA , United States 6 Institute for Biomedical Informatics, University of Pennsylvania , Philadelphia, PA , United States 2 Biomedical & Translational Informatics Institute, Geisinger , Danville, PA , United States |
| AuthorAffiliation_xml | – name: 1 Department of Software and Computer Engineering, Ajou University , Suwon , South Korea – name: 6 Institute for Biomedical Informatics, University of Pennsylvania , Philadelphia, PA , United States – name: 5 Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA , United States – name: 4 Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine , Indianapolis, IN , United States – name: 2 Biomedical & Translational Informatics Institute, Geisinger , Danville, PA , United States – name: 3 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine , Indianapolis, IN , United States |
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| Cites_doi | 10.1093/bib/bbk007 10.1038/nature14539 10.1109/I2C2.2017.8321780 10.1109/TBME.2015.2404809 10.1155/2017/5485080 10.1038/s41598-018-22871-z 10.1016/j.neuroimage.2011.09.069 10.1109/72.279181 10.1038/srep39880 10.1016/j.nicl.2013.05.004 10.1158/1078-0432.CCR-17-0853 10.1093/jamia/ocw112 10.1038/s41598-018-37769-z 10.1101/114892 10.1371/journal.pone.0033182 |
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| Keywords | gated recurrent unit python package Alzheimer’s disease data integration multimodal deep learning |
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| References | Chaudhary (B3) 2018; 24 Choi (B5) 2017; 24 Zhang (B15) 2012; 59 Lama (B8) 2017; 2017 Zhang (B16) 2012; 7 Chaudhary (B2) 2017; 853 Lee (B11) 2019; 9 Cheng (B4) 2015; 62 Deng (B6) 2013 LeCun (B10) 2015; 521 Huang (B7) 2017; 7 Bengio (B1) 1994; 5 Sandeep (B13) 2017; 1 Young (B14) 2013; 2 Lu (B12) 2018; 8 Larranaga (B9) 2006; 7 |
| References_xml | – volume: 7 start-page: 86 year: 2006 ident: B9 article-title: Machine learning in bioinformatics publication-title: Briefings Bioinf. doi: 10.1093/bib/bbk007 – volume: 521 start-page: 436 year: 2015 ident: B10 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 1 start-page: 35 year: 2017 ident: B13 article-title: Feature extraction of MRI brain images for the early detection of alzheimer’s disease publication-title: Bioprocess Eng. doi: 10.1109/I2C2.2017.8321780 – volume: 62 start-page: 1805 year: 2015 ident: B4 article-title: Domain Transfer Learning for MCI Conversion Prediction publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2015.2404809 – volume: 2017 start-page: 11 year: 2017 ident: B8 article-title: Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features publication-title: J. Healthcare Eng. doi: 10.1155/2017/5485080 – volume: 8 start-page: 5697 year: 2018 ident: B12 article-title: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural mr and fdg-pet images publication-title: Sci. Rep. doi: 10.1038/s41598-018-22871-z – volume: 59 start-page: 895 year: 2012 ident: B15 article-title: Initiative AsDN: multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.09.069 – volume: 5 start-page: 157 year: 1994 ident: B1 article-title: Learning long-term dependencies with gradient descent is difficult publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.279181 – volume: 7 year: 2017 ident: B7 article-title: Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease publication-title: Sci. Rep. doi: 10.1038/srep39880 – volume: 2 start-page: 735 year: 2013 ident: B14 article-title: Initiative AsDN: accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment publication-title: NeuroImage Clin. doi: 10.1016/j.nicl.2013.05.004 – volume: 24 start-page: 1248 year: 2018 ident: B3 article-title: Deep learning–based multi-omics integration robustly predicts survival in liver cancer publication-title: Clin. Can. Res. doi: 10.1158/1078-0432.CCR-17-0853 – volume: 24 start-page: 361 year: 2017 ident: B5 article-title: Using recurrent neural network models for early detection of heart failure onset publication-title: J. Am. Med. Inf. Assoc. doi: 10.1093/jamia/ocw112 – volume: 9 start-page: 1952 year: 2019 ident: B11 article-title: Predicting Alzheimer's disease progression using multi-modal deep learning approach publication-title: Sci. Rep. doi: 10.1038/s41598-018-37769-z – start-page: 8599 volume-title: IEEE International Conference on: 2013 year: 2013 ident: B6 article-title: New types of deep neural network learning for speech recognition and related applications: An overview – volume: 853 start-page: 1246 year: 2017 ident: B2 article-title: Deep learning based multi-omics integration robustly predicts survival in liver cancer publication-title: Clin. Can. Res. doi: 10.1101/114892 – volume: 7 year: 2012 ident: B16 article-title: Initiative AsDN: predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers publication-title: PloS One doi: 10.1371/journal.pone.0033182 |
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| Title | MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework |
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