Advanced Statistical and Machine Learning Approaches for High-Dimensional Biomedical Data
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".
Deadline for manuscript submissions: 26 February 2026 | Viewed by 9
Special Issue Editor
Special Issue Information
Dear Colleagues,
The increasing availability of complex datasets in biomedical fields, such as genomics, transcriptomics, proteomics, and medical imaging, presents unique challenges and opportunities. This Special Issue will highlight the development and application of advanced statistical and machine learning models for analyzing high-dimensional biomedical data. Recognizing the growing complexity and volume of biomedical data, this Special Issue seeks contributions that present novel methodological advancements in statistical learning and machine learning algorithms (including deep learning) specifically tailored for handling the challenges inherent in high-dimensional datasets.
We encourage submissions that focus on the development of innovative models, algorithms, and frameworks, as well as their practical application to extract meaningful insights from complex biomedical data such as genomics, imaging, and clinical records. Topics of interest include the creation of interpretable models, the development of robust feature selection methods, the integration of diverse data sources, and the rigorous validation of predictive and inferential models with clear relevance to biological discovery and clinical translation.
Dr. Jingyi (Ginny) Zheng
Guest Editor
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Keywords
- biomedical data analysis
- statistical learning
- machine learning
- deep learning
- high-dimensional data analysis
- biostatistics
- bioinformatics
- predictive modeling
- feature selection
- dimensionality reduction
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