Bayesian Learning and Its Applications in Genomics
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 29 November 2024 | Viewed by 3107
Special Issue Editor
Special Issue Information
Dear Colleagues,
We are pleased to announce the Special Issue of “Bayesian Learning and its Applications in Genomics”. In recent decades, a vast amount of genomics data on an unprecedented scale and complexity have been made accessible for developing and applying the cutting-edge Bayesian learning techniques, including fully Bayesian analysis and scalable Bayesian methods. Bayesian methods can offer a principled framework to model complex genomic structure, integrate prior biological information, and make probabilistic inferences for better understanding the etiology of complex diseases.
We kindly invite you to contribute your original research, reviews, or software articles to diverse aspects of Bayesian learning in genomics that include, but are not limited to, the following topics:
- Bayesian methods for integrative genomics and multi-omics integration;
- Bayesian machine learning for genomics data with complex disease traits (including categorical, survival, longitudinal, functional and neuroimaging phenotypes);
- Bayesian methods for single-cell genomics and spatial transcriptomics;
- Bayesian learning to infer complex structure in genomics data including gene regulatory networks, gene–gene and gene–environment interactions;
- Bayesian causal inference in genomics;
- Bayesian approaches for genetic association studies and Genome-Wide Association Studies.
Dr. Cen Wu
Guest Editor
Manuscript Submission Information
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Keywords
- Bayesian learning
- complex diseases
- entropy
- high-dimensional genomics data
- Markov chain Monte Carlo (MCMC)
- scalable Bayesian inference
- uncertainty quantification
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.