Submit to CIMB Review for CIMB Propose a Special Issue

Journal Menu

Journal Browser

Challenges and Advances in Bioinformatics and Computational Biology

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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: closed (31 October 2025) | Viewed by 13297

Share This Special Issue

Special Issue Editor

Dr. Haijun Gong

E-Mail Website
Guest Editor

Department of Mathematics and Statistics, Saint Louis University, St. Louis, MO 63103, USA
Interests: statistics; machine learning; 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 entitled "Challenges and Advances in Bioinformatics and Computational Biology". This Special Issue aims to highlight the latest research, developments, and innovative approaches in bioinformatics and computational biology, with a particular focus on the molecular biology level.

We welcome original research articles, comprehensive reviews, and insightful perspectives that address significant challenges and advancements in the analysis, interpretation, and application of molecular data. Topics of interest include the following:

Novel computational methods for molecular data analysis;
Advances in genomics, proteomics, and metabolomics;
Integration and interpretation of multi-omics data;
Computational modeling of molecular interactions and pathways;
Applications of machine learning and AI in bioinformatics;
Big data analytics in molecular biology;
Bioinformatics tools for molecular diagnostics and personalized medicine;
Data mining and visualization techniques for molecular data.

This Special Issue seeks to gather contributions from leading scientists and researchers who are at the forefront of molecular bioinformatics and computational biology. Submissions should provide deep insights, propose innovative methodologies, and present significant case studies or applications that advance our understanding of molecular biology through computational approaches. We also thank Dr. Tong Si for her contribution to this Special Issue.

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.

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
AI
big data
molecular diagnostics
personalized medicine
data visualization

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research

4 pages, 167 KB

Open AccessEditorial

Challenges and Advances in Bioinformatics and Computational Biology

by Tong Si and Haijun Gong

Curr. Issues Mol. Biol. 2026, 48(2), 185; https://doi.org/10.3390/cimb48020185 - 6 Feb 2026

Viewed by 857

Abstract

Modern sequencing and high-throughput profiling technologies [...] Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

Research

Jump to: Editorial

24 pages, 3872 KB

Open AccessArticle

Genes and Gene Functions Associated with Morphological, Productive, Reproductive, and Carcass Quality Traits in Pigs: A Functional Bioinformatics Approach

by Wilber Hernández-Montiel, Víctor M. Meza-Villalvazo, Dany A. Dzib-Cauich, Juan M. Zaldívar-Cruz, José Abad-Zavaleta, Nubia Noemi Cob-Calan, Nicolás Valenzuela-Jiménez, Roberto Zamora-Bustillos and Amada I. Osorio-Terán

Curr. Issues Mol. Biol. 2026, 48(2), 153; https://doi.org/10.3390/cimb48020153 - 30 Jan 2026

Cited by 1 | Viewed by 773

Abstract

Understanding the functional mechanisms of genes influencing economically important traits in the domestic pig is essential for optimizing marker-assisted selection (MAS). This study aimed to characterize the biological functions, molecular mechanisms, and metabolic pathways of genes associated with morphological, productive, reproductive, and carcass quality traits through a functional bioinformatics approach. Genes were compiled from 116 peer-reviewed studies published between 2000 and 2024, and subsequently grouped according to trait. A de novo functional bioinformatics analysis was performed on this dataset. Functional enrichment analysis was conducted using DAVID and the clusterProfiler package in R, applying FDR correction (≤0.05). Protein-protein interaction (PPI) networks were explored using STRING. No individual gene was consistently reported with high frequency. Among the most frequently reported genes were VRTN (17 studies) for teat number, HOMER1 (3 studies) for leg strength, and BMPR1B (3 studies) for litter size. Enriched GO terms included processes such as positive regulation of transcription (GO:0045944), chondrocyte differentiation (GO:0032331), and SMAD signaling (GO:0060391; an FDR = 7.34 × 10⁻⁷). The PPI networks revealed key genes involved in signaling and immune regulation. In conclusion, this bioinformatics analysis provides an integrated functional overview of the genes underlying key economic traits in pigs, identifying pleiotropic pathways such as SMAD/TGF-β signaling, which supports the development of more effective MAS strategies in pig breeding programs. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Figure 1

19 pages, 6498 KB

Open AccessArticle

Dihydromyricetin Remodels the Tumor Immune Microenvironment in Hepatocellular Carcinoma: Development and Validation of a Prognostic Model

by Yang Xu, Chao Gu, Wei Li, Fei Lan, Jingkun Mao, Xiao Tan and Pengfei Li

Curr. Issues Mol. Biol. 2025, 47(12), 1010; https://doi.org/10.3390/cimb47121010 - 2 Dec 2025

Cited by 1 | Viewed by 741

Abstract

Background: Dihydromyricetin (DHM), a natural dihydroflavonol, exhibits diverse pharmacological properties, including anti-inflammatory, antioxidant, and anti-tumor effects. However, its potential mechanism of action in the individualized therapy of hepatocellular carcinoma (HCC) remains unclear. Methods: Potential therapeutic targets of DHM were identified using the Swiss Target Prediction database. The overlap between these targets and differentially expressed genes in HCC was analyzed to determine therapeutic targets. A prognostic model was constructed based on these genes, and patients were stratified into high- and low-risk groups. The associations between risk scores, clinical pathological characteristics, and overall survival were analyzed using Cox regression and Kaplan–Meier survival curves. The relationships between risk score and immune cell infiltration, immunosuppressive factors, and anticancer drug susceptibility were evaluated. Results: A three-gene prognostic model was established, comprising DTYMK, MAPT, and UCK2, designated as DHM-target genes (DHMGs). Patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (p < 0.001; HR [95% CI] = 4.953 [2.544, 9.645]). Higher risk scores were correlated with more advanced tumor stages and grades. Comprehensive analysis of the tumor immune microenvironment revealed that high-risk patients exhibited significantly elevated TIDE scores, increased Treg cell infiltration, and markedly reduced stromal scores. Conclusions: This study developed a prognostic model based on the potential target genes of DHM in HCC. This model effectively stratifies HCC patients, identifying a high-risk subgroup characterized by an immunosuppressive microenvironment. These findings provide a theoretical foundation for exploring DHM as a promising natural adjuvant for cancer immunotherapy. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Figure 1

13 pages, 2545 KB

Open AccessArticle

PixelCut: A Unified Solution for Zero-Configuration 16S rRNA Trimming via Computer Vision

by Dongin Kim, Woo Jin Kim, Hyun-Myung Woo and Hyundoo Jeong

Curr. Issues Mol. Biol. 2025, 47(12), 968; https://doi.org/10.3390/cimb47120968 - 21 Nov 2025

Cited by 1 | Viewed by 824

Abstract

16S rRNA amplicon sequencing has been an effective method for profiling microbial taxonomy in microbiome research, as it offers lower per-sample costs and higher sample throughput than shotgun metagenomics. Although 16S rRNA sequencing offers clear advantages over shotgun sequencing, it depends on precise trimming of low-quality bases at the 3′ ends of reads. Given the widespread use of 16S rRNA amplicon sequencing, there is an increasing demand for analysis tools that can identify errors in the 3′ region of reads and remove erroneous bases. While various algorithms for predicting trim locations are widely employed, most are command-line standalone tools, which pose challenges for users with limited computational background or resources. Furthermore, in the absence of biological or experimental priors such as amplicon size, trim position predictions may be unreliable. Here, we introduce PixelCut, a fully automated trim-position prediction framework that requires no hyperparameters or prior biological information for accurate prediction. Unlike most available algorithms that operate on raw FASTQ data, PixelCut analyzes the per-base quality report generated by FastQC to infer trimming positions. Based on the recommended quality score threshold from the quality report, PixelCut inspects the quality scores across bases and automatically determines the recommended trim position using character recognition techniques based on computer vision. We have also developed a user-friendly web application to make the method accessible to those without programming expertise, while offering a command-line version for advanced users. Through comprehensive computer simulations, we show that PixelCut produces taxonomic profiling results that are consistent with those from popular trim-location prediction algorithms. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Figure 1

26 pages, 25630 KB

Open AccessArticle

Constructing a Pan-Cancer Prognostic Model via Machine Learning Based on Immunogenic Cell Death Genes and Identifying NT5E as a Biomarker in Head and Neck Cancer

by Luojin Wu, Qing Sun, Atsushi Kitani, Xiaorong Zhou, Liming Mao and Mengmeng Sang

Curr. Issues Mol. Biol. 2025, 47(10), 812; https://doi.org/10.3390/cimb47100812 - 1 Oct 2025

Cited by 3 | Viewed by 1421

Abstract

Immunogenic cell death (ICD) is a specialized form of cell death that triggers antitumor immune responses. In tumors, ICD promotes the release of tumor-associated and tumor-specific antigens, thereby reshaping the immune microenvironment, restoring antitumor immunity, and facilitating tumor eradication. However, the regulatory mechanisms of ICD and its immunological effects vary across tumor types, and a comprehensive understanding remains limited. We systematically analyzed the expression of 34 ICD-related regulatory genes across 33 tumor types. Differential expression at the RNA, copy number variation (CNV), and DNA methylation levels was assessed in relation to clinical features. Associations between patient survival and RNA expression, CNVs, single-nucleotide variations (SNVs), and methylation were evaluated. Patients were stratified into immunological subtypes and further divided into high- and low-risk groups based on optimal prognostic models built using a machine learning framework. We explored the relationships between ICD-related genes and immune cell infiltration, stemness, heterogeneity, immune scores, immune checkpoint and regulatory genes, and subtype-specific expression patterns. Moreover, we examined the influence of immunotherapy and anticancer immune responses, applied three machine learning algorithms to identify prognostic biomarkers, and performed drug prediction and molecular docking analyses to nominate therapeutic targets. ICD-related genes were predominantly overexpressed in ESCA, GBM, KIRC, LGG, PAAD, and STAD. RNA expression of most ICD-related genes was associated with poor prognosis, while DNA methylation of these genes showed significant survival correlations in LGG and UVM. Prognostic models were successfully established for 18 cancer types, revealing intrinsic immune regulatory mechanisms of ICD-related genes. Machine learning identified several key prognostic biomarkers across cancers, among which NT5E emerged as a predictive biomarker in head and neck squamous cell carcinoma (HNSC), mediating tumor–immune interactions through multiple ligand–receptor pairs. This study provides a comprehensive view of ICD-related genes across cancers, identifies NT5E as a potential biomarker in HNSC, and highlights novel targets for predicting immunotherapy response and improving clinical outcomes in cancer patients. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Figure 1

18 pages, 2279 KB

Open AccessArticle

MvAl-MFP: A Multi-Label Classification Method on the Functions of Peptides with Multi-View Active Learning

by Yuxuan Peng, Jicong Duan, Yuanyuan Dan and Hualong Yu

Curr. Issues Mol. Biol. 2025, 47(8), 628; https://doi.org/10.3390/cimb47080628 - 6 Aug 2025

Cited by 1 | Viewed by 1307

Abstract

The rapid expansion of peptide libraries and the increasing functional diversity of peptides have highlighted the significance of predicting the multifunctional properties of peptides in bioinformatics research. Although supervised learning methods have made advancements, they typically necessitate substantial amounts of labeled data for yielding accurate prediction. This study presents MvAl-MFP, a multi-label active learning approach that incorporates multiple feature views of peptides. This method takes advantage of the natural properties of multi-view representation for amino acid sequences, meets the requirement of the query-by-committee (QBC) active learning paradigm, and further significantly diminishes the requirement for labeled samples while training high-performing models. First, MvAl-MFP generates nine distinct feature views for a few labeled peptide amino acid sequences by considering various peptide characteristics, including amino acid composition, physicochemical properties, evolutionary information, etc. Then, on each independent view, a multi-label classifier is trained based on the labeled samples. Next, a QBC strategy based on the average entropy of predictions across all trained classifiers is adopted to select a specific number of most valuable unlabeled samples to submit them to human experts for labeling by wet-lab experiments. Finally, the aforementioned procedure is iteratively conducted with a constantly expanding labeled set and updating classifiers until it meets the default stopping criterion. The experiments are conducted on a dataset of multifunctional therapeutic peptides annotated with eight functional labels, including anti-bacterial properties, anti-inflammatory properties, anti-cancer properties, etc. The results clearly demonstrate the superiority of the proposed MvAl-MFP method, as it can rapidly improve prediction performance while only labeling a small number of samples. It provides an effective tool for more precise multifunctional peptide prediction while lowering the cost of wet-lab experiments. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Figure 1

23 pages, 722 KB

Open AccessArticle

Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data

by Yunge Wang, Lingling Zhang, Tong Si, Sarah Roberts, Yuqi Wang and Haijun Gong

Curr. Issues Mol. Biol. 2025, 47(6), 408; https://doi.org/10.3390/cimb47060408 - 30 May 2025

Cited by 3 | Viewed by 2957

Abstract

Inferring time-varying gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data remains a challenging task. The existing methods have notable limitations as most are either designed for reconstructing time-varying networks from bulk microarray data or constrained to inferring stationary networks from scRNA-seq data, failing to capture the dynamic regulatory changes at the single-cell level. Furthermore, scRNA-seq data present unique challenges, including sparsity, dropout events, and the need to account for heterogeneity across individual cells. These challenges complicate the accurate capture of gene regulatory network dynamics over time. In this work, we propose a novel f-divergence-based dynamic gene regulatory network inference method (f-DyGRN), which applies f-divergence to quantify the temporal variations in gene expression across individual single cells. Our approach integrates a first-order Granger causality model with various regularization techniques and partial correlation analysis to reconstruct gene regulatory networks from scRNA-seq data. To infer dynamic regulatory networks at different stages, we employ a moving window strategy, which allows for the capture of dynamic changes in gene interactions over time. We applied this method to analyze both simulated and real scRNA-seq data from THP-1 human myeloid monocytic leukemia cells, comparing its performance with the existing approaches. Our results demonstrate that f-DyGRN, when equipped with a suitable f-divergence measure, outperforms most of the existing methods in reconstructing dynamic regulatory networks from time-series scRNA-seq data. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Figure 1

22 pages, 1862 KB

Open AccessArticle

DNA Gene’s Basic Structure as a Nonperturbative Circuit Quantum Electrodynamics: Is RNA Polymerase II the Quantum Bus of Transcription?

by Raul Riera Aroche, Yveth M. Ortiz García, Esli C. Sánchez Moreno, José S. Enriquez Cervantes, Andrea C. Machado Sulbaran and Annie Riera Leal

Curr. Issues Mol. Biol. 2024, 46(11), 12152-12173; https://doi.org/10.3390/cimb46110721 - 30 Oct 2024

Cited by 4 | Viewed by 2739

Abstract

Previously, we described that Adenine, Thymine, Cytosine, and Guanine nucleobases were superconductors in a quantum superposition of phases on each side of the central hydrogen bond acting as a Josephson Junction. Genomic DNA has two strands wrapped helically around one another, but during transcription, they are separated by the RNA polymerase II to form a molecular condensate called the transcription bubble. Successive steps involve the bubble translocation along the gene body. This work aims to modulate DNA as a combination of n-nonperturbative circuits quantum electrodynamics with nine Radio-Frequency Superconducting Quantum Interference Devices (SQUIDs) inside. A bus can be coupled capacitively to a single-mode microwave resonator. The cavity mode and the bus can mediate long-range, fast interaction between neighboring and distant DNA SQUID qubits. RNA polymerase II produces decoherence during transcription. This enzyme is a multifunctional biomolecular machine working like an artificially engineered device. Phosphorylation catalyzed by protein kinases constitutes the driving force. The coupling between

n

-phosphorylation pulses and any particular SQUID qubit can be obtained selectively via frequency matching. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

► Show Figures

Journal Menu

Journal Browser

Challenges and Advances in Bioinformatics and Computational Biology

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI