Author:
Yang Yin,Xu Shuangbin,Hong Yifan,Cai Yantong,Tang Wenli,Wang Jiao,Shen Bairong,Zong Hui,Yu Guangchuang
Abstract
<p>Biomedical data encompasses images, texts, physiological signals, and molecular omics data. As the costs of various data acquisition methods, such as genomic sequencing, continue to decrease, the availability of biomedical data is increasing. However, this data often exhibits high dimensionality, heterogeneity, and multimodal characteristics, necessitating the use of advanced computational modeling. Transforming raw data into meaningful biological insights is a critical aspect of computational modeling, which plays an increasingly important role in biomedical research in the era of big data. This review outlines the collection of various types of biomedical data and the challenges faced in data modeling, including high dimensionality, standardization, and privacy protection. Additionally, it addresses the complexity and interpretability of models used to guide knowledge discoveries. The review also discusses computational architectures such as parallel computing, cloud computing, and edge computing, which are essential to meet the demands of large-scale computation. Furthermore, it highlights the driving force of computational modeling in advancing medical research. With the foundation of big data, big models, and big computation, biomedical research is transitioning from experimental observation to theoretical deduction and data-driven approaches, profoundly impacting scientific research methodologies and paradigms. The development of biomedical computational modeling is steering medical research toward intelligent medicine, redefining the scientific research paradigm in biomedicine.</p>
Publisher
Innovation Press Co., Limited
Cited by
1 articles.
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