MSPL: Multimodal Self-Paced Learning for Multi-Omics Feature Selection and Data Integration

Rapid advances in high-throughput sequencing technology have led to the generation of a large number of multi-omics biological datasets. Integrating data from different omics provides an unprecedented opportunity to gain insight into disease mechanisms from different perspectives. However, integrati...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.170513-170524
Main Authors: Yang, Zi-Yi, Xia, Liang-Yong, Zhang, Hui, Liang, Yong
Format: Article
Language:English
Subjects:
Analytical models
Biology
classification
Confidence
Data integration
Data models
Datasets
Diseases
Feature extraction
feature selection
Heterogeneity
Learning
Multi-omics data integration
multimodal data analysis
Performance evaluation
Prediction models
Predictive models
self-paced learning
Sequences
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Summary:Rapid advances in high-throughput sequencing technology have led to the generation of a large number of multi-omics biological datasets. Integrating data from different omics provides an unprecedented opportunity to gain insight into disease mechanisms from different perspectives. However, integrative analysis and predictive modeling from multi-omics data are facing three major challenges: i) heavy noises; ii) the high dimensions compared to the small samples; iii) data heterogeneity. Current multi-omics data integration approaches have some limitations and are susceptible to heavy noise. In this paper, we present MSPL, a robust supervised multi-omics data integration method that simultaneously identifies significant multi-omics signatures during the integration process and predicts the cancer subtypes. The proposed method not only inherits the generalization performance of self-paced learning but also leverages the properties of multi-omics data containing correlated information to interactively recommend high-confidence samples for model training. We demonstrate the capabilities of MSPL using simulated data and five multi-omics biological datasets, integrating up three omics to identify potential biological signatures, and evaluating the performance compared to state-of-the-art methods in binary and multi-class classification problems. Our proposed model makes multi-omics data integration more systematic and expands its range of applications.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2955958