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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 March 2026) | Viewed by 7334

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Dr. Howard H. Yang

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National Cancer Institute (NCI), Bethesda, MD, USA
Interests: bioinformatics; biostatistics; genetics and genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

In this Special Issue, "Featured Papers in Bioinformatics and Systems Biology", we aim to publish high-quality research articles, communications, and review articles in the fields of bioinformatics and systems biology. Artificial Intelligence (AI) and deep learning have significantly impacted bioinformatics and systems biology. We encourage the submission of manuscripts that present innovative and the state-of-the-art research on the applications of AI and deep learning tools and methods in bioinformatics and systems biology for understanding complex biological systems, including the following areas: genomics and transcriptomics sequence analyses; protein structure prediction; epigenomics: histone modification, DNA methylation, and chromatin accessibility; network inference and analysis; dynamic modeling of biological systems; multi-omics data integration; evolutionary biology; data integration and systems biology; biomedical image analysis; AI modeling for understanding disease mechanisms; drug discovery and development; biomedical image analysis; and disease risk prediction and personalized medicine.

Dr. Howard H. Yang
Guest Editor

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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.

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Keywords

bioinformatics
omics
genomics and transcriptomics sequence analyses
dynamic modeling

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Published Papers (5 papers)

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Research

12 pages, 16476 KB

Open AccessArticle

OATP1B3 c.699G>A Predicts a 6.3-Fold Increased Risk of Hyperbilirubinemia During OPrD Therapy for HCV

by Zuhal Altintas and Engin Altintas

Curr. Issues Mol. Biol. 2026, 48(5), 452; https://doi.org/10.3390/cimb48050452 - 27 Apr 2026

Viewed by 277

Abstract

Although ombitasvir/paritaprevir/ritonavir plus dasabuvir (OPrD) therapy is highly effective for chronic hepatitis C (CHC), clinicians frequently encounter transient hyperbilirubinemia, which can be misidentified as hepatotoxicity. This study investigated the role of SLCO1B1 (OATP1B1) and SLCO1B3 (OATP1B3) genetic polymorphisms in predicting bilirubin spikes and distinguishing transporter-mediated interference from hepatocellular injury. In this prospective study of 65 patients with HCV genotype 1, genotyping for OATP1B1 (c.388A>G, c.521T>C) and OATP1B3 (c.334T>G, c.699G>A) was performed using PCR-RFLP and capillary electrophoresis (QIAxcel Advanced System). Clinical and biochemical parameters were monitored over a 12-week treatment period. Hyperbilirubinemia (total bilirubin >1.1 mg/dL) developed in 18.5% of the cohort, typically within the first month. A distinct ‘AST-dominant’ biochemical signature, elevated bilirubin and AST paired with stable ALT, was identified, suggesting transporter-specific interference rather than hepatocyte damage. Statistical analysis pinpointed the OATP1B3 c.699G>A (rs7311358) variant as the sole genetic driver (p = 0.007). Carriers of the c.699G>A allele faced a 6.3-fold higher risk of developing hyperbilirubinemia (OR: 6.30, 95% CI: 1.48–26.80, p = 0.032), while no significant associations were found for OATP1B1 variants. We conclude that OATP1B3 c.699G>A is a potent predictor of OPrD-induced hyperbilirubinemia. Identifying this genotype pre-treatment allows clinicians to anticipate transient, benign bilirubin elevations and prevent unnecessary drug discontinuation, thereby mitigating therapeutic inertia and ensuring treatment continuity for CHC patients. Full article

(This article belongs to the Special Issue Featured Papers in Bioinformatics and Systems Biology)

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19 pages, 10797 KB

Open AccessArticle

Integrative Multi-Omics and Machine Learning Identify ID1 as a Candidate Gene Associated with Abdominal Aortic Aneurysm

by Feng Guo, Michael Keese, Yu Zhao and Qining Fu

Curr. Issues Mol. Biol. 2026, 48(2), 156; https://doi.org/10.3390/cimb48020156 - 30 Jan 2026

Viewed by 3188

Abstract

Abdominal aortic aneurysm (AAA) is a fatal vascular disorder driven by immune dysregulation and extracellular matrix (ECM) degradation, yet its molecular mechanisms remain unclear. This study investigated the mechanistic role of ID1 in AAA using an integrative multi-omics and machine learning approach. Two bulk transcriptomic datasets (GSE232911 and GSE183464) were analyzed through differential expression, WGCNA, and three machine learning algorithms (LASSO, Random Forest, and SVM-RFE), followed by immune infiltration analysis via ssGSEA and CIBERSORT. ID1 and CYP4B1 were identified by all three machine learning algorithms, but only ID1 showed stable downregulation and consistent discriminatory ability across independent datasets. (AUC = 0.939 and 0.868). Functional enrichment and immune deconvolution linked low ID1 expression to enhanced adaptive immune signaling, increased M1 macrophages, γδ T cells, and memory B cells, and reduced neutrophil and mast cell activity. Single-cell RNA sequencing (GSE226492) confirmed endothelial- and fibroblast-specific ID1 downregulation in AAA. These findings identify ID1 as a candidate gene associated with vascular immune remodeling and extracellular matrix–related pathways, providing a basis for future mechanistic investigation in AAA. Full article

(This article belongs to the Special Issue Featured Papers in Bioinformatics and Systems Biology)

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29 pages, 9251 KB

Open AccessArticle

Using Genome-Wide Association Studies to Reveal DArTseq and SNP Loci Associated with Agronomic Traits and Yield in Maize

by Maciej Lenort, Agnieszka Tomkowiak, Jan Bocianowski, Roksana Bobrowska, Danuta Kurasiak-Popowska, Sylwia Mikołajczyk, Tomasz Kosiada, Dorota Weigt and Przemysław Gawrysiak

Curr. Issues Mol. Biol. 2025, 47(12), 1008; https://doi.org/10.3390/cimb47121008 - 30 Nov 2025

Cited by 1 | Viewed by 871

Abstract

Next-generation sequencing (NGS) has revolutionized genetic research, enabling the massive, rapid, and relatively inexpensive analysis of the genomes, transcriptomes, and epigenomes of various organisms, including maize. Therefore, this paper uses NGS, association mapping, and physical mapping to identify candidate genes associated with yield structure traits and yield in maize (Zea mays L.). Furthermore, expression analysis of selected candidate genes was performed to confirm their contribution to yield formation. The plant material used for the study was 186 F1 hybrids and 20 reference genotypes (high-yielding and low-yielding). Field experiments were conducted simultaneously in two locations (in Smolice and Kobierzyce). NGS yielded a total of 45,876 molecular markers (24,437 SilicoDArT markers and 21,439 SNP markers) relevant to yield and crop structure. The largest number of markers in both localities (Smolice and Kobierzyce) was related to: the number of grain rows (6960), dry matter content after harvest (6616), the number of grains in a row (6721), mass of grain from the cob (6616), and cob length (6564). The smallest number of markers in both localities was related to yield (t ha⁻¹) (1114) and yield from the plot (1237). To narrow down the number of markers for physical mapping, ten were selected from all the significant ones associated with the same traits in both localities (Kobierzyce and Smolice). Significant markers included eight silicoDArT markers (459199, 2447305, 4768759, 4579916, 4764335, 2448946, 2492509, 4774802) and two SNP markers (9692004, 5587791). These markers were used for physical mapping. These markers are located on chromosomes 7, 8, and 10. Some of these markers are located at a considerable distance from characterized genes or within uncharacterized genes. Two markers caught our attention: SNP 5587791 and silicoDArT 4774802. The first one is located on chromosome 8 inside exon 5 of the LOC100383455 U-box domain-containing protein 7 gene, the second marker is also located on chromosome 8 near (300 bp) the LOC103635953 putative WUSCHEL-related homeobox 2 protein gene. Our own research and literature reports indicate the usefulness of next-generation sequencing, association mapping, and physical mapping for identifying candidate genes associated with economically important traits in maize. Furthermore, two genes characterized in detail in the publication, LOC100383455 U-box domain-containing protein 7 gene and LOC103635953 putative WUSCHEL-related homeobox 2 protein gene, may be involved in processes related to maize yield. Full article

(This article belongs to the Special Issue Featured Papers in Bioinformatics and Systems Biology)

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31 pages, 4430 KB

Open AccessArticle

Genetic Evidence Prioritizes Neurocognitive Decline as a Causal Driver of Sleep Disturbances: A Multi-Omics Analysis Identifying Causal Genes and Therapeutic Targets

by Yanan Du, Xiao-Yong Xia, Zhu Ni, Sha-Sha Fan, Junwen He, Yang He, Xiang-Yu Meng, Xu Wang and Xuan Xu

Curr. Issues Mol. Biol. 2025, 47(11), 967; https://doi.org/10.3390/cimb47110967 - 20 Nov 2025

Cited by 2 | Viewed by 1463

Abstract

To resolve the ambiguous causal relationship between sleep disturbances and neurodegenerative diseases such as Alzheimer’s disease (AD), we conducted a multi-stage genetic and multi-omics investigation. Our large-scale bidirectional Mendelian randomization analysis identified a robust, asymmetrical pattern of genetic association, providing strong genetic evidence suggesting that liability for neurocognitive decline and AD is associated with sleep disturbances, with substantially weaker evidence for the reverse direction. To identify the underlying molecular drivers, a multi-omics Summary-data-based MR (SMR) analysis prioritized high-confidence causal genes, including YWHAZ, NT5C2, COX6B1, and CDK10. The predictive power of this gene signature was confirmed using machine learning models (ROC-AUC > 0.8), while functional validation through bulk and single-cell transcriptomics uncovered profound, cell-type-specific dysregulation in the AD brain, most notably opposing expression patterns between neurons and glial cells (e.g., YWHAZ was upregulated in excitatory neurons but downregulated in glia). Functional enrichment and network analyses implicated two core pathways—nucleotide metabolism centered on NT5C2 and synaptic function involving YWHAZ—and our investigation culminated in the identification of a promising therapeutic interaction, with molecular docking validating high-affinity binding between Ecdysterone and COX6B1 (docking score = −5.73 kcal/mol). Collectively, our findings strengthen the evidence that sleep disruption as a likely consequence of neurodegenerative processes and prioritize a set of validated, cell-type-specific gene targets within critical pathways, offering promising new avenues for therapeutic development. Full article

(This article belongs to the Special Issue Featured Papers in Bioinformatics and Systems Biology)

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14 pages, 3126 KB

Open AccessArticle

CAMF-DTI: Enhancing Drug–Target Interaction Prediction via Coordinate Attention and Multi-Scale Feature Fusion

by Jia Mi, Chang Li, Daguang Jiang and Jing Wan

Curr. Issues Mol. Biol. 2025, 47(11), 964; https://doi.org/10.3390/cimb47110964 - 20 Nov 2025

Cited by 1 | Viewed by 1064

Abstract

The accurate prediction of drug–target interactions is essential for drug discovery and development. However, current models often struggle with two challenges. First, they fail to model the directional flow and positional sensitivity of protein sequences, which are critical for identifying functional interaction regions. Second, they lack mechanisms to integrate multi-scale information from both local binding sites and broader structural context. To overcome these limitations, we propose CAMF-DTI, a novel framework that incorporates coordinate attention, multi-scale feature fusion, and cross-attention to enhance both the representation and interaction learning of drug and protein features. Drug molecules are represented as molecular graphs and encoded using graph convolutional networks, while protein sequences are processed with coordinate attention to preserve directional and spatial information. Multi-scale fusion modules are applied to both encoders to capture local and global features, and a cross-attention module integrates the representations to enable dynamic drug–target interaction modeling. We evaluate CAMF-DTI on four benchmark datasets: BindingDB, BioSNAP, C.elegans, and Human. Experimental results show that CAMF-DTI consistently outperforms seven state-of-the-art baselines in terms of AUROC, AUPRC, Accuracy, F1-score, and MCC. Ablation studies further confirm the effectiveness of each module, and visualization results demonstrate the model’s potential interpretability. Full article

(This article belongs to the Special Issue Featured Papers in Bioinformatics and Systems Biology)

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