A call has gone out for papers for the special issue 'Advances in Statistical Methods and Machine Learning for Medical and Genetic Epidemiology' in the journal Mathematics. Guest editor, RSS fellow and member of council, Ayse Ulgen has invited researchers to contribute their cutting-edge work in this rapidly evolving field.
This special issue aims to showcase innovative developments in statistical and computational approaches for analysing complex biomedical and genetic data, Submissions that integrate machine learning and artificial intelligence techniques with traditional epidemiological methods to enhance our understanding of disease etiology, risk factors, and health trajectories are welcomed.
Key focus areas include:
- Novel machine learning algorithms for high-dimensional genetic and biomedical data
- Advanced statistical methods for causal inference in observational studies
- Innovative approaches to integrate multiple data types (genomic, environmental, clinical)
- Improved predictive modeling techniques for disease risk and prognosis
- Methods for analyzing large-scale population-based studies and biobanks
- We encourage contributions from researchers in epidemiology, biostatistics, genetics and computer science, addressing challenges in analysing complex biological systems and heterogeneous datasets.
Submission Deadline: August 20, 2025
Contact Ayse Ulgen for more information, or visit the Mathematics website.