Jianjun Wang

BGI Genomics Co., Ltd., Shenzhen, 518083, Guangdong, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 4   doi: 10.5376/cmb.2024.14.0019
Received: 09 Jun., 2024    Accepted: 28 Jul., 2024    Published: 12 Aug., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wang J.J., 2024, Biostatistical challenges in high-dimensional data analysis: strategies and innovations, Computational Molecular Biology, 14(4): 163-172 (doi: 10.5376/cmb.2024.14.0019)

In contemporary biological research, the emergence of high-dimensional data has become the norm, especially in fields such as genomics, transcriptomics, and metabolomics. With the widespread application of high-dimensional data, researchers must adopt appropriate strategies to address issues of data sparsity, multicollinearity, and heterogeneity. This study not only summarizes existing dimensionality reduction, regularization, and ensemble learning methods, but also discusses innovative technologies such as machine learning, deep learning, and multi omics data integration to address high-dimensional problems in biological data, providing effective strategies and cutting-edge methods for researchers and data scientists.

High-dimensional data; Biostatistical challenges; Machine learning; Multi-omics data integration; Regularization methods