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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 456

Special Issue Editor

Department of Statistics, Kansas State University, Manhattan, KS 66506 USA
Interests: high dimensional data; statistical machine learning; data analysis; statistical genetics; bioinformatics

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bayesian learning
  • complex diseases
  • entropy
  • high-dimensional genomics data
  • Markov chain Monte Carlo (MCMC)
  • scalable Bayesian inference
  • uncertainty quantification

Published Papers

This special issue is now open for submission.
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