Machine Learning in Biomedical Research: Application, Innovation and Exploration

Dear Colleagues,

As a core technology in the field of artificial intelligence, machine learning is transforming research across various disciplines at an unprecedented pace, with its interdisciplinary integration with biomedicine being particularly striking. With the advancement of modern measurement technologies, biomedical research has generated massive volumes of data, covering (but not limited to) multiple dimensions such as routine clinical data, cohort follow-up data, gene sequencing data, medical imaging data, and clinical medical records. Classical analytical methods are gradually revealing limitations in processing such complex data, while machine learning—endowed with powerful capabilities in pattern recognition, data analysis, and prediction—has brought new perspectives and tools to biomedical research, emerging as a crucial driving force for advancing biomedical studies. Based on this, we have curated this Special Issue, aiming to gather cutting-edge research achievements in this field, facilitate academic exchange and collaboration, and further promote the innovative application and development of machine learning in biomedicine.

The content of this Special Issue includes, but is not limited to, the following topics:

• Innovation and application of machine learning methods in biomedical data analysis: Examples include methodological research and practical applications related to the fusion and modeling of high-dimensional multimodal data, as well as the modeling (data governance) of medical imaging data and functional data (e.g., ECG or EEG measurements).
• Development and application of machine learning algorithms in precision medicine: This includes, but is not limited to, machine learning-based analysis of individualized treatment strategies, novel methods for heterogeneous treatment effect estimation and subgroup analysis, and innovative applications of existing methods.
• Disease diagnosis and prediction: This includes, but is not limited to, the integration of multi-source information (such as patients’ genetic data, clinical history, lifestyle, and environmental factors) to construct novel disease risk prediction models.
• Drug discovery and screening: In the stage of drug target identification, machine learning algorithms can conduct in-depth analysis of massive biomedical data (including genomics and proteomics data) to explore potential disease-related drug targets. The development and application of relevant methods are also within the focus of this Special Issue.
• Challenges of machine learning methods in biomedical data analysis: Despite the remarkable achievements of machine learning in biomedical applications, it still faces numerous challenges, such as data privacy and security, data quality control (missing data and data governance), etc. Work in these areas is also a focus of this Special Issue.
• Other important issues regarding the application of machine learning methods in biomedicine.

Dr. Fangyao Chen
Dr. Xianjun Li
Dr. Wu Qiu
Guest Editors

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