Machine Learning Approaches for Multi-omics Data Integration in Medicine-Reference-Cited by-同舟云学术

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Machine Learning Approaches for Multi-omics Data Integration in Medicine

Published:2023-11-14 Issue: Volume: Page:23-38
ISSN:
Container-title:Machine Learning Methods for Multi-Omics Data Integration
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Author:

Yagin Fatma Hilal

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-36502-7_3

Reference62 articles.

1. Abdi, H., Williams, L. J., & Valentin, D. (2013). Multiple factor analysis: Principal component analysis for multitable and multiblock data sets. Wiley Interdisciplinary Reviews: Computational Statistics, 5, 149–179.

2. Ahmed, Z. (2020). Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Human Genomics, 14, 1–5.

3. Ahmed, Z. (2022). Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. Progress in Molecular Biology and Translational Science, 190, 101–125.

4. Akbulut, S., Yagin, F. H., & Colak, C. (2022). Prediction of breast cancer distant metastasis by artificial intelligence methods from an epidemiological perspective. Istanbul Medical Journal, 23, 210–215.

5. Alcala, N., Leblay, N., Gabriel, A., Mangiante, L., Hervás, D., Giffon, T., Sertier, A.-S., Ferrari, A., Derks, J., & Ghantous, A. (2019). Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids. Nature Communications, 10, 1–21.

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1. Medical Statistics in Drug Development and Regulatory Approval;Advances in Bioinformatics and Biomedical Engineering;2024-07-18

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