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Featured Papers in Bioinformatics and Systems 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: 31 March 2026 | Viewed by 1525

Special Issue Editor

Dr. Howard H. Yang

E-Mail Website
Guest Editor
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

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
  • omics
  • genomics and transcriptomics sequence analyses
  • dynamic modeling

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

Published Papers (3 papers)

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Research

29 pages, 9251 KB  
Article
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
Viewed by 268
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 [...] Read more.
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  
Article
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 1 | Viewed by 642
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 [...] Read more.
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  
Article
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
Viewed by 427
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. [...] Read more.
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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