A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimizat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/7/3650 |
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| Summary: | High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimization techniques, including regularization and constraint-based approaches, have been already employed for regression and binary classification problems. Building on these methods, we extended this framework to support multi-class classification. Indeed, applied to a multi-class classification task for breast cancer subtypes, our model achieves accuracy between 73% and 81% under various block-wise missing data scenarios. Additionally, we assess its performance on a regression problem using the exposome dataset, integrating a larger number of omics datasets. Across different missing data scenarios, our model demonstrates a strong correlation (75%) between true and predicted responses. Furthermore, we have updated the bwm R package, which previously supported binary and continuous response types, to also include multi-class response types. |
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| ISSN: | 2076-3417 |