Computational modeling for medical data: From data collection to knowledge discovery

Yin Yang; Shuangbin Xu; Yifan Hong; Yantong Cai; Wenli Tang; Jiao Wang; Bairong Shen; Hui Zong; Guangchuang Yu

doi:10.59717/j.xinn-life.2024.100079

The Innovation Life > 2024 Vol. 2 > No. 3 > 100079

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Computational modeling for medical data: From data collection to knowledge discovery

1.
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
2.
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
3.
Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
4.
Dermatology Hospital, Southern Medical University, Guangzhou 266003, China
5.
These authors contributed equally

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Corresponding authors: gcyu1@smu.edu.cn (G.Y.); zonghui0228@163.com (H.Z.); bairong.shen@scu.edu.cn (B.S.)
Received Date: April 13, 2024

Accepted Date: July 20, 2024

Published Online: August 02, 2024

The Innovation Life 2(3), https://doi.org/10.59717/j.xinn-life.2024.100079 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Medical big data has created high demand for computational modeling.
  
  Growing data size and complexity need advanced models and more computational power.
  
  Integrating big data, large-scale models, and advanced computation drives intelligent medical research.

ABSTRACT

ABSTRACT

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.

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Yang Y., Xu S., Hong Y., et al., (2024). Computational modeling for medical data: From data collection to knowledge discovery. The Innovation Life 2(3): 100079. https://doi.org/10.59717/j.xinn-life.2024.100079

Yang Y., Xu S., Hong Y., et al., (2024). Computational modeling for medical data: From data collection to knowledge discovery. The Innovation Life 2(3): 100079. https://doi.org/10.59717/j.xinn-life.2024.100079

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