Gene Clustering in Microbiological and Biotechnological Research

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biochemical Engineering".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 503

Special Issue Editor


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Guest Editor
Division of Biostatistics, Division of Biostatistics, Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI 53226, USA
Interests: biostatistics; survival analysis; machine learning for survival data; high-dimensional data analysis; missing data analysis

Special Issue Information

Dear Colleagues,

With the advent of precision medicine, where the patient treatments are selected based on the genetic understanding of their diseases, it is important to understand the relationship between genes and their complex mechanisms. Gene pathways and gene regulatory networks describe the relationship between genes based on biological experiments, but many remain unexplored. Unlike traditional approaches which rely on a single type of omics data, mostly only gene expression, this Special Issue would like to address the ongoing challenge of identifying gene relationships using innovative microbiological and biotechnological approaches, with focus on the integration of multiple omics datasets to enhance the accuracy of gene clustering analysis. The key objectives include developing novel algorithms for integrating multiple omics data to understand the underlying biological process, presenting application in the fields of microbiology and biotechnology, and discussing the potential implications for personalized treatment strategies using an improved gene clustering algorithm. In summary, this Special Issue invites original research articles that contribute to the integrative analysis using multiple omics data for gene clustering algorithm in microbiological and biotechnological research.

Prof. Dr. Kwang Woo Ahn
Guest Editor

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Keywords

  • gene pathway
  • gene clustering algorithm
  • integrative analysis
  • ulti-omics
  • precision medicine
  • microbiology
  • biotechnology

Published Papers (1 paper)

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Research

15 pages, 407 KiB  
Article
Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data
by Ulrich Kemmo Tsafack, Kwang Woo Ahn, Anne E. Kwitek and Chien-Wei Lin
Bioengineering 2024, 11(6), 587; https://doi.org/10.3390/bioengineering11060587 - 8 Jun 2024
Viewed by 283
Abstract
Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes [...] Read more.
Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods. Full article
(This article belongs to the Special Issue Gene Clustering in Microbiological and Biotechnological Research)
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