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Entropy
Special Issues
Bayesian Learning and Its Applications in Genomics
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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 534

Special Issue Editor

Dr. Cen Wu

E-Mail Website
Guest 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, see below for planned papers.

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.

 
Title: Is seeing believing? A practitioner’s perspective on high-dimensional statistical inference in cancer genomics studies
 
Abstract: Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with the complex disease traits including cancer outcomes. However, the reliability and reproducibility of findings is in question if valid inferential procedures are not available to quantify the uncertainty of findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models that can yield uncertainty quantification measures including confidence (or Bayesian credible) intervals, p-values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the ``Unpenalized loss function + Penalty term" formulation for regularization methods and the ``Likelihood function $\times$ Sparse-prior" framework for sparse Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies, due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. Numerical results show that robust Bayesian analysis has accurately yielded not only the estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared to alternative frequentist and Bayesian methods, especially under the heavy-tailed model errors and outliers.
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