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Emerging Trends in Bioinformatics and Computational Biology

A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1199

Special Issue Editor


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Department of Mathematics & Statistics, Saint Louis University, St. Louis, MO 63103, USA
Interests: statistics; machine learning; deep learning; model checking; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue titled “Emerging Trends in Bioinformatics and Computational Biology”. This issue will showcase cutting-edge research and innovative methodologies that are shaping the future of bioinformatics and computational biology, with a strong emphasis on molecular-level biological applications.

We welcome high-quality original research papers, reviews, and perspective articles that explore current challenges and breakthroughs in the computational analysis of molecular data. Topics of interest include, but are not limited to, the following:

  • Novel algorithms and computational methods for molecular data analysis;
  • Advances in genomics, transcriptomics, proteomics, and metabolomics;
  • Multi-omics data integration and systems-level insights;
  • Computational modeling of molecular mechanisms and regulatory network inference;
  • Applications of machine learning, deep learning, and AI in bioinformatics;
  • Missing value imputation and change-point detection;
  • Big data approaches for molecular biology research;
  • Novel methods for molecular diagnostics, precision medicine, and therapeutics;
  • Data visualization and mining techniques for high-dimensional molecular data.

This Special Issue aims to bring together contributions from worldwide researchers in the fields of bioinformatics and computational biology. Submissions should provide new insights, introduce transformative methods, or present impactful applications that deepen our understanding of molecular biology through computational innovation.

Notably, we also want to thank the journal’s Topical Advisory Panel Member, Dr. Tong Si, for her contribution and support to the Special Issue operation, promotion and development of this Special Issue.

Prof. Dr. Haijun Gong
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 250 words) can be sent to the Editorial Office for assessment.

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. Current Issues in Molecular Biology 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 2200 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

  • bioinformatics
  • computational biology
  • genomics
  • proteomics
  • metabolomics
  • multi-omics
  • machine learning
  • algorithms
  • big data
  • molecular diagnostics
  • personalized medicine
  • data visualization

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

Published Papers (1 paper)

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Research

20 pages, 16568 KB  
Article
Scissor–CIBERSORTx Deconvolution Reveals Functional Heterogeneity of CTAL/aTAL Cells and Associated Biomarkers in Renal Fibrosis
by Hengping Wang, Yuan Zhang, Jiale Li, Ying Fu and Huiyan Wang
Curr. Issues Mol. Biol. 2026, 48(2), 215; https://doi.org/10.3390/cimb48020215 - 16 Feb 2026
Viewed by 432
Abstract
Renal fibrosis (RF) represents a major pathological outcome of chronic kidney disease, currently accompanied by extremely limited therapeutic strategies. To decipher key cellular and molecular drivers, we integrated single-cell and bulk transcriptomic profiles for comprehensive analysis. Based on the RF-related single-cell and bulk [...] Read more.
Renal fibrosis (RF) represents a major pathological outcome of chronic kidney disease, currently accompanied by extremely limited therapeutic strategies. To decipher key cellular and molecular drivers, we integrated single-cell and bulk transcriptomic profiles for comprehensive analysis. Based on the RF-related single-cell and bulk transcriptomic data, key cell subtypes were identified through Scissor analysis, custom signature matrix construction via CIBERSORTx, and Weighted Gene Co-Expression Network Analysis (WGCNA). Subsequently, key subtype-related biomarkers were identified through the expression analysis, and functional enrichment analysis for biomarkers was conducted to elucidate the potential mechanisms by which biomarkers regulate RF. Through comprehensive profiling, thick ascending limb (TAL) cells were predominant and displayed marked heterogeneity in renal fibrosis (RF), with cortical TAL (CTAL) and adaptive TAL (aTAL) identified as principal subtypes. A set of candidate biomarkers was identified. Quantitative polymerase chain reaction (qPCR) validation in mouse models confirmed aberrant expression of these biomarkers, with STAT1 and PARP8 upregulated and HS6ST2, PTGER3, and TMEM207 downregulated in RF. Furthermore, functional enrichment analyses indicated that these biomarkers were associated with pathways underlying metabolic reprogramming and immune perturbation. Our study implicates CTAL and aTAL as central cellular players in RF and identifies their associated biomarkers. These experimentally validated biomarkers provide novel targets and repurposing opportunities for RF therapeutic intervention. Full article
(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)
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