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Mathematics
Special Issues
Statistical Methods and Applications in Genetics and Genomics
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Statistical Methods and Applications in Genetics and Genomics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3924

Special Issue Editors

Prof. Dr. Zilin Li

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Northeast Normal University, Changchun, China
Interests: statistical genetics and genomics; high-dimensional statistics
Dr. Xihao Li

E-Mail Website
Guest Editor
Department of Biostatistics and Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Interests: biostatistics; statistical genetics and multi-omics; machine learning

Special Issue Information

Dear Colleagues,

High-throughput sequencing technologies have catalyzed the rapid growth of large-scale complex genetics, genomics and multi-omics data. For example, the open-access UK Biobank data collects several petabytes of genetics, genomics, epidemiological and health data to investigate the genetic bases of human diseases and traits. However, the scale and complexity of such data pose significant challenges to traditional computational and statistical methods, both in terms of computational scalability and statistical efficiency.

To address these issues, this Special Issue aims to bridge this gap by providing a collection of articles that focus on innovative statistical methodologies and their applications in genetics genomics, and multi-omics data. This Special Issue will feature a selection of papers demonstrating the integration of advanced mathematical theories, statistical and machine/deep learning methods, , and computational algorithms to address complex problems in analyzing genetics, genomics and multi-omics data. The topics covered include statistical genetics and genomics, population genetics, bioinformatics, and computational biology. The objective is to advance the mathematical and statistical foundations of genomics research and enhance our understanding of the genetic underpinnings of various diseases and traits, facilitating the development of personalized medicine and healthcare strategies.

Prof. Dr. Zilin Li
Dr. Xihao Li
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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

  • statistical genetics and genomics
  • multi-omics
  • biostatistics
  • bioinformatics
  • computational biology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

13 pages, 426 KiB  
Article
Transfer Elastic Net for Developing Epigenetic Clocks for the Japanese Population
by Yui Tomo and Ryo Nakaki
Mathematics 2024, 12(17), 2716; https://doi.org/10.3390/math12172716 - 30 Aug 2024
Cited by 1 | Viewed by 2370
Abstract
The epigenetic clock evaluates human biological age based on DNA methylation patterns. It takes the form of a regression model where the methylation ratio at CpG sites serves as the predictor and age as the response variable. Due to the large number of [...] Read more.
The epigenetic clock evaluates human biological age based on DNA methylation patterns. It takes the form of a regression model where the methylation ratio at CpG sites serves as the predictor and age as the response variable. Due to the large number of CpG sites and their correlation, Elastic Net is commonly used to train the models. However, existing standard epigenetic clocks, trained on multiracial data, may exhibit biases due to genetic and environmental differences among specific racial groups. Developing epigenetic clocks suitable for a specific single-race population requires collecting and analyzing hundreds or thousands of samples, which costs a lot of time and money. Therefore, an efficient method to construct accurate epigenetic clocks with smaller sample sizes is needed. We propose Transfer Elastic Net, a transfer learning approach that trains a model in the target population using the information of parameters estimated by the Elastic Net in a source population. Using this method, we constructed Horvath’s, Hannum’s, and Levine’s types of epigenetic clocks from blood samples of 143 Japanese subjects. The DNA methylation data were transformed through principal component analysis to obtain more reliable clocks. The developed clocks demonstrated the smallest prediction errors compared to both the original clocks and those trained with the Elastic Net on the same Japanese data. Transfer Elastic Net can also be applied to develop epigenetic clocks for other specific populations, and is expected to be applied in various fields. Full article
(This article belongs to the Special Issue Statistical Methods and Applications in Genetics and Genomics)
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13 pages, 885 KiB  
Article
Propagation Computation for Mixed Bayesian Networks Using Minimal Strong Triangulation
by Yao Liu, Shuai Wang, Can Zhou and Xiaofei Wang
Mathematics 2024, 12(13), 1925; https://doi.org/10.3390/math12131925 - 21 Jun 2024
Viewed by 810
Abstract
In recent years, mixed Bayesian networks have received increasing attention across various fields for probabilistic reasoning. Though many studies have been devoted to propagation computation on strong junction trees for mixed Bayesian networks, few have addressed the construction of appropriate strong junction trees. [...] Read more.
In recent years, mixed Bayesian networks have received increasing attention across various fields for probabilistic reasoning. Though many studies have been devoted to propagation computation on strong junction trees for mixed Bayesian networks, few have addressed the construction of appropriate strong junction trees. In this work, we establish a connection between the minimal strong triangulation for marked graphs and the minimal triangulation for star graphs. We further propose a minimal strong triangulation method for the moral graph of mixed Bayesian networks and develop a polynomial-time algorithm to derive a strong junction tree from this minimal strong triangulation. Moreover, we also focus on the propagation computation of all posteriors on this derived strong junction tree. We conducted multiple numerical experiments to evaluate the performance of our proposed method, demonstrating significant improvements in computational efficiency compared to existing approaches. Experimental results indicate that our minimal strong triangulation approach provides a robust framework for efficient probabilistic inference in mixed Bayesian networks. Full article
(This article belongs to the Special Issue Statistical Methods and Applications in Genetics and Genomics)
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