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Wang XW, Wang T, Schaub DP, Chen C, Sun Z, Ke S, Hecker J, Maaser-Hecker A, Zeleznik OA, Zeleznik R, Litonjua AA, DeMeo DL, Lasky-Su J, Silverman EK, Liu YY, Weiss ST. Benchmarking omics-based prediction of asthma development in children. Respir Res 2023; 24:63. [PMID: 36842969 PMCID: PMC9969629 DOI: 10.1186/s12931-023-02368-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/16/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. OBJECTIVE We aimed to investigate the computational methods in disease status prediction using multi-omics data. METHOD We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. RESULTS Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. CONCLUSIONS Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.
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Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Darius P Schaub
- Department of Mathematics, University of Hamburg, 21109, Hamburg, Germany
| | - Can Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Zheng Sun
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Shanlin Ke
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Anna Maaser-Hecker
- Genetics and Aging Research Unit, Department of Neurology, McCance Center for Brain Health, Mass General Institute for Neurodegenerative Disease, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Roman Zeleznik
- Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonology, Golisano Children's Hospital, Rochester, NY, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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