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Advances in Bioinformatics and Environmental Health
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Advances in Bioinformatics and Environmental Health

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 7475

Special Issue Editors

Dr. Huang Lin

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA
Interests: bioinformatics; machine learning; causal inference; infectious disease
Dr. Zhen Chen

E-Mail Website
Guest Editor
Biostatistics and Bioinformatics Branch (BBB), Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD), National Institutes of Health (NIH), Bethesda, MD 20892, USA
Interests: Bayesian modeling and computations; diagnostic accuracy; causal inference; chemical mixture modeling
Dr. Ruijin Lu

E-Mail Website
Guest Editor Assistant
Division of Biostatistics, St. Louis School of Medicine, Washington University, St. Louis, MO 63110, USA
Interests: Alzheimer’s disease biomarker; biological signal; (chemical) mixture exposures study

Special Issue Information

Dear Colleagues,

In today’s world, where environmental factors intricately influence health, bioinformatics emerges as a key player in deciphering these complex relationships. Our Special Issue, titled “Advances in Bioinformatics and Environmental Health”, is dedicated to exploring the dynamic interplay between environmental exposures, various biomarkers, and health outcomes.

The fusion of biostatistics with bioinformatics has revolutionized our ability to identify and analyze biomarkers, shedding light on the health effects of environmental changes. In this era of data-centric science, applying bioinformatics in environmental health is pivotal for unraveling the multifaceted interactions among environmental exposures, biomarker variations, and health impacts. We invite pioneering contributions that demonstrate innovative bioinformatics approaches within the environmental health sphere. Our focus extends to research developing and applying bioinformatics tools for biomarker identification and analysis, including, but not limited to, genetics, epigenetics, genomics, metagenomics, and metabolomics, as well as their connections to environmental stressors such as pollutants, chemical mixtures, extreme weather events, and their consequent health effects.

We encourage submissions from experts in the field, including research articles and critical reviews, to foster advancement in the realms of bioinformatics and environmental health.

Sincerely,

Dr. Huang Lin
Dr. Zhen Chen
Guest Editors

Dr. Ruijin Lu
Guest Editor Assistant

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. Genes 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

  • biostatistics
  • bioinformatics
  • biomarkers
  • environmental health
  • environmental exposure

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Published Papers (5 papers)

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Research

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19 pages, 10518 KiB  
Article
Deciphering Gut Microbiome in Colorectal Cancer via Robust Learning Methods
by Huiye Han, Ying Li, Youran Qi, Stefano Mangiola and Wodan Ling
Genes 2025, 16(4), 452; https://doi.org/10.3390/genes16040452 - 15 Apr 2025
Viewed by 501
Abstract
Background: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and is closely linked to the gut microbiota. Identifying reproducible and generalizable microbial signatures holds significant potential for enhancing early detection and advancing treatment for this deadly disease. Methods: This study [...] Read more.
Background: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and is closely linked to the gut microbiota. Identifying reproducible and generalizable microbial signatures holds significant potential for enhancing early detection and advancing treatment for this deadly disease. Methods: This study integrated various publicly available case-control datasets to identify microbial signatures for CRC. Alpha and beta diversity metrics were evaluated to characterize differences in gut microbial richness, evenness, and overall composition between CRC patients and healthy controls. Differential abundance analysis was conducted using ANCOM-BC and LEfSe to pinpoint individual taxa that were enriched or depleted in CRC patients. Additionally, sccomp, a Bayesian machine learning method from single-cell analysis, was adapted to provide a more robust validation of compositional differences in individual microbial markers. Results: Gut microbial richness is significantly higher in CRC patients, and overall microbiome composition differs significantly between CRC patients and healthy controls. Several taxa, such as Fusobacterium and Peptostreptococcus, are enriched in CRC patients, while others, including Anaerostipes, are depleted. The microbial signatures identified from the integrated data are reproducible and generalizable, with many aligning with findings from previous studies. Furthermore, the use of sccomp enhanced the precision of individual microbial marker identification. Conclusions: Biologically, the microbial signatures identified from the integrated data improve our understanding of the gut microbiota’s role in CRC pathogenesis and may contribute to the development of translational targets and microbiota-based therapies. Methodologically, this study demonstrates the effectiveness of adapting robust techniques from single-cell research to improve the precision of microbial marker discovery. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Environmental Health)
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10 pages, 626 KiB  
Article
A Study of Short-Chain Fatty Acids During the Canalicular and Early Saccular Phases of Fetal Lung Development and Childhood Asthma
by Huang Lin, Neil J. Perkins, Flory Nkoy, Joseph B. Stanford, Karen C. Schliep and Shyamal D. Peddada
Genes 2024, 15(12), 1595; https://doi.org/10.3390/genes15121595 - 13 Dec 2024
Cited by 1 | Viewed by 1047
Abstract
Background: Emerging literature indicates that the microbiome and its byproducts, such as short-chain fatty acids (SCFAs), play an important role in childhood diseases such as allergies and asthma. Specifically, there is evidence suggesting that SCFAs play a critical role in fetal immunoprogramming during [...] Read more.
Background: Emerging literature indicates that the microbiome and its byproducts, such as short-chain fatty acids (SCFAs), play an important role in childhood diseases such as allergies and asthma. Specifically, there is evidence suggesting that SCFAs play a critical role in fetal immunoprogramming during the late saccular phase of fetal lung development. An increase in acetate during the late saccular phase is known to play a critical role in inhibiting histone deacetylases (HDACs), resulting in a cascade of events, including Treg immune regulation, involved in fetal immunoprogramming, and reduction in the asthma phenotype. However, it is not known whether changes in SCFA levels, especially acetate, occurred during the canalicular or early saccular phase among pregnant women whose children did not develop asthma. Methods: In this research, we investigated this question using plasma samples obtained from mothers during the 20th and 28th weeks of pregnancy. Mothers whose children developed asthma were categorized as cases, while those whose children did not were categorized as controls. The specimens were assayed for a panel of SCFAs consisting of acetate, propionate, butyrate, valerate, isobutyrate, and isovalerate. Results: The resulting data indicated no significant differences between the cases and controls, either at week 20 or week 28, in any of the SCFAs measured, despite the vascularization during these phases. Conclusions: We did not find differences in measured SCFAs at week 20 or at week 28. A larger prospective study covering multiple time points is necessary to confirm the findings of this preliminary study. Such a study, together with the published literature regarding later time points, may help discover critical windows during pregnancy when simple manipulation of diet will result in healthier outcomes for infants. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Environmental Health)
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13 pages, 1475 KiB  
Article
Nongenetic and Genetic Factors Associated with White Matter Brain Aging: Exposome-Wide and Genome-Wide Association Study
by Li Feng, Halley S. Milleson, Zhenyao Ye, Travis Canida, Hongjie Ke, Menglu Liang, Si Gao, Shuo Chen, L. Elliot Hong, Peter Kochunov, David K. Y. Lei and Tianzhou Ma
Genes 2024, 15(10), 1285; https://doi.org/10.3390/genes15101285 - 30 Sep 2024
Cited by 1 | Viewed by 2073
Abstract
Background/Objectives: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying [...] Read more.
Background/Objectives: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals. Methods: In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors. Results: We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging. Conclusions: The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Environmental Health)
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17 pages, 893 KiB  
Article
On the Optimal Combination of Elliptically Distributed Biomarkers to Improve Diagnostic Accuracy
by Shiqi Dong, Zhaohai Li, Yuanzhang Li and Aiyi Liu
Genes 2024, 15(9), 1145; https://doi.org/10.3390/genes15091145 - 30 Aug 2024
Viewed by 1083
Abstract
Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, [...] Read more.
Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, as most biomarkers follow distributions that deviate from normality. While the likelihood ratio combination is recognized to be the optimal approach, it is complicated to calculate. To achieve a more accurate and effective combination of biomarkers, especially when these biomarkers deviate from normality, we propose using a receiver operating characteristic (ROC) curve methodology based on the optimal combination of elliptically distributed biomarkers. In this paper, we derive the ROC curve function for the elliptical likelihood ratio combination. Further, proceeding from the derived best combinations of biomarkers, we propose an efficient technique via nonparametric maximum likelihood estimate (NPMLE) to build empirical estimation. Simulation results show that the proposed elliptical combination method consistently provided better performance, demonstrating its robustness in handling various distribution types of biomarkers. We apply the proposed method to two real datasets: Autism/autism spectrum disorder (ASD) and neural tube defects (NTD). In both applications, the elliptical likelihood ratio combination improves the AUC value compared to the multivariate normal likelihood ratio combination and the best linear combination. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Environmental Health)
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Review

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16 pages, 974 KiB  
Review
Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference
by Shelley H. Liu, Ellerie S. Weber, Katherine E. Manz, Katharine J. McCarthy, Yitong Chen, Peter J. Schüffler, Carolyn W. Zhu and Melissa Tracy
Genes 2024, 15(11), 1457; https://doi.org/10.3390/genes15111457 - 12 Nov 2024
Viewed by 2010
Abstract
Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level [...] Read more.
Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level interventions, may impact AD/ADRD disease burden at the population level in real-world settings and the cost-effectiveness of interventions. Methods: We performed a critical review to discuss the challenges in modeling exposome interventions on population-level AD/ADRD burden and the potential of using agent-based modeling (ABM) and other advanced data science methods for causal inference to achieve this. Results: We describe how ABM can be used for empirical causal inference modeling and provide a virtual laboratory for simulating the impacts of personal and policy-level interventions. These hypothetical experiments can provide insight into the optimal timing, targeting, and duration of interventions, identifying optimal combinations of interventions, and can be augmented with economic analyses to evaluate the cost-effectiveness of interventions. We also discuss other data science methods, including structural equation modeling and Mendelian randomization. Lastly, we discuss challenges in modeling the complex exposome, including high dimensional and sparse data, the need to account for dynamic changes over time and over the life course, and the role of exposome burden scores developed using item response theory models and artificial intelligence to address these challenges. Conclusions: This critical review highlights opportunities and challenges in modeling exposome interventions on population-level AD/ADRD disease burden while considering the cost-effectiveness of different interventions, which can be used to aid data-driven policy decisions. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Environmental Health)
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