Advances in Statistical Methods and Machine Learning for Medical and Genetic Epidemiology
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 46
Special Issue Editor
Special Issue Information
Dear Colleagues,
The Special Issue on "Advances in Statistical Methods and Machine Learning for Medical and Genetic Epidemiology" aims to showcase cutting-edge developments in statistical and computational approaches for analyzing complex biomedical and genetic data. This Special Issue highlights the integration of machine learning and artificial intelligence techniques with traditional epidemiological methods to enhance our understanding of disease etiology, risk factors, and health trajectories across the human lifespan.
Key focus areas include the following:
- Novel machine learning algorithms for handling high-dimensional genetic and biomedical data;
- Advanced statistical methods for causal inference in observational studies;
- Innovative approaches to integrate multiple data types, including genomic, environmental, and clinical data;
- Improved predictive modeling techniques for disease risk and prognosis;
- Methods for analyzing large-scale population-based studies and biobanks.
This Special Issue brings together contributions from researchers focusing on epidemiology, biostatistics, genetics, and computer science to address challenges in analyzing complex biological systems and heterogeneous datasets.
It aims to provide insights into the application of these advanced methods in real-world biomedical scenarios, from identifying genetic risk factors to developing personalized interventions.
Dr. Ayse Ulgen
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- artificial intelligence
- genetic epidemiology
- causal inference
- predictive modeling
- big-data analytics
- biostatistics
- precision medicine
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