https://scholars.lib.ntu.edu.tw/handle/123456789/628341
標題: | Quantitative analysis of high-throughput biological data | 作者: | HSUEH-FEN JUAN Huang, Hsuan Cheng |
關鍵字: | biological network | data integration | multiomics data | quantitative analysis | single-cell transcriptomics | 公開日期: | 1-一月-2023 | 出版社: | John Wiley and Sons Inc | 來源出版物: | Wiley Interdisciplinary Reviews: Computational Molecular Science | 摘要: | The study of multiple “omes,” such as the genome, transcriptome, proteome, and metabolome has become widespread in biomedical research. High-throughput techniques enable the rapid generation of high-dimensional multiomics data. This multiomics approach provides a more complete perspective to study biological systems compared with traditional methods. However, the quantitative analysis and integration of distinct types of high-dimensional omics data remain a challenge. Here, we provide an up-to-date and comprehensive review of the methods used for omics data quantification and integration. We first review the quantitative analysis of not only bulk but also single-cell transcriptomics data, as well as proteomics data. Current methods for reducing batch effects and integrating heterogeneous high-dimensional data are then introduced. Network analysis on large-scale biomedical data can capture the global properties of drugs, targets, and disease relationships, thus enabling a better understanding of biological systems. Current trends in the applications and methods used to extend quantitative omics data analysis to biological networks are also discussed. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/628341 | ISSN: | 17590876 | DOI: | 10.1002/wcms.1658 |
顯示於: | 生命科學系 |
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