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        <title>Frontiers in Bioinformatics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics</link>
        <description>RSS Feed for Frontiers in Bioinformatics | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-09T17:49:05.486+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1719535</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1719535</link>
        <title><![CDATA[Characterizing miRNA editing patterns in 5 types of cells using single-cell small RNA sequencing data]]></title>
        <pubdate>2026-04-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunyi Mao</author><author>Hao Guo</author><author>Wenping Xie</author><author>Yue Xu</author><author>Hongjia Zhang</author><author>Kang Luo</author><author>Jun Yang</author><author>Yun Zheng</author>
        <description><![CDATA[Numerous studies have identified a large number of miRNA editing sites via deep sRNA sequencing profiling of tissue samples. However, the single-cell landscape of miRNA editing patterns has remained largely unknown to date. To investigate miRNA editing and mutation characteristics at single cell level, this study analyzed miRNA editing and mutation events in 448 single-cell small RNA sequencing profiles from 5 different cell types. Our results revealed that PCA and clustering analysis, performed based on the editing levels of identified miRNA editing sites, could distinguish distinct cell types, indicating that miRNA editing patterns are cell-type-specific across different cellular populations. We further demonstrated that a subset of miRNA editing sites exhibited strict cell-type-specific editing patterns. Meanwhile, within the same cell type, the identified sites presented different distributions of editing levels in different cells. A fraction of sites showed highly variable editing levels among different cells of the same cell type, while some sites displayed relatively uniform and consistent editing patterns. An A-to-I editing site in hsa-mir-376c, i.e., hsa-mir-376c 48 A g, showed a significantly higher editing level in glioblastoma cells than in naive embryonic stem cells, suggesting a potential role in the initiation and progression of glioblastoma. Furthermore, our results also suggest that in leukemia cells, TENT4A, TENT5A, TENT5B, TENT5C, TENT5D, and TUT1 may mediate the non-templated nucleotide additions to the 3′ends of miRNAs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1677453</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1677453</link>
        <title><![CDATA[Bioinformatics strategies and biomarker refinement using high-throughput transcriptome data in transplantation]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Oliver P. Günther</author><author>Karen R. Sherwood</author><author>Franz Fenninger</author><author>Robert F. Balshaw</author><author>Andreas Scherer</author><author>Zsuzsanna Hollander</author><author>Raymond Ng</author><author>Janet Wilson-McManus</author><author>W. Robert McMaster</author><author>Bruce M. McManus</author><author>Paul A. Keown</author>
        <description><![CDATA[IntroductionRenal transplantation is the treatment of choice for kidney failure, but most transplants fail prematurely and barely half of recipients survive with a functioning graft for more than a decade. Strategies to induce operational tolerance are therefore at the cutting edge of transplant research, exploiting the dynamic plasticity of the immune system to recapitulate neonatal ontogeny and permit gradual withdrawal of immune suppression. We have shown that whole blood gene expression is profoundly altered in uremia and following graft implantation, and that changes in the blood transcriptome are characteristic of rejection injury. But deriving simple, robust and parsimonious classifiers presents challenges, and pre-filtering methods of varying stringency have been proposed to enhance predictive accuracy.MethodsWe re-analyzed our previous data documenting transcriptome changes in rejection using a case-control design to compare analytical strategies in subjects with or without biopsy-proven rejection. Five pre-filtering methods and eight multivariate classification methods were evaluated using multiple partition nested cross-validation to obtain unbiased estimation of classifier performance.ResultsThe most permissive strategy identified 800 unique genes and the most restrictive identified 71 nested genes differentially expressed in rejectors of which 31%–45% were downregulated and 55%–69% were upregulated, reflecting neutrophil degranulation, regulated necrosis, programmed cell death, pyroptosis, interleukin signaling and other functional pathways. Of the ten most common genes or probe-sets over all panels, nine were increased in BCAR.DiscussionNo individual combination of methods presented superior performance among all those considered although the PAM and XGBoost classifiers were more resistant to over-fitting. It is therefore advisable to apply multiple analytical combinations and compare performances in transcriptome analysis. In limited resource situations, evaluation of at least two complementary classifiers with fixed pre-filter and ranking methods is advisable. For small panel size constraints, feature-selecting methods like PAM or EN could be considered.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1766384</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1766384</link>
        <title><![CDATA[Transcriptomics-driven identification of CDK1 as a central oncogenic driver in TNBC: an in silico structural modeling and MD simulation approach]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Uma Chaudhary</author><author>Sidharth Kumar Nanda Kumar</author><author>Magesh Ramaswamy</author><author>Mythili Asaithambi</author>
        <description><![CDATA[IntroductionTriple-negative breast cancer (TNBC) is an aggressive subtype that lacks ER, PR, and HER2 receptors, which limits the availability of targeted therapies. In this study, we analyzed CDK1 as a potential molecular target and evaluated natural compounds that might inhibit its activity.MethodsTranscriptomic comparison revealed 85 commonly upregulated mRNAs in TNBC, and functional enrichment combined with PPI network analysis indicated CDK1 as a major hub gene. To search for potential inhibitors, we screened an anticancer-focused phytochemical library from the SuperNatural 3.0 database using molecular docking followed by ADMET assessment.ResultsAmong the screened molecules, CID17584963 showed the strongest binding energy (−8.09 kcal/mol) and displayed pharmacokinetic properties comparable to or better than those of paclitaxel. Long-timescale (500 ns) molecular dynamics simulations further supported the stability of the CDK1–CID17584963 complex, with root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, solvent-accessible surface area (SASA), hydrogen-bond profiles, and principal component analysis (PCA) all indicating consistent interactions throughout the trajectory.DiscussionTaken together, these findings indicate that CID17584963 interacts with CDK1 more stably than the reference drug and may serve as a promising natural compound for further studies in TNBC therapy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1755843</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1755843</link>
        <title><![CDATA[Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Xiaoyu Zhou</author><author>Weijing Tao</author>
        <description><![CDATA[The pharmaceutical research and development (R&D) process is persistently challenged by high financial costs, protracted timelines, and remarkably low success rates. Artificial intelligence (AI) technology, by simulating complex biological systems, has accelerated the innovation of the entire drug discovery pipeline. This review positions AI as a pivotal technology for reengineering the R&D process by utilizing sophisticated molecular representations to predict pharmacodynamic (PD) and toxicological effects significantly earlier. The scope systematically covers the AI foundations in chemoinformatics, detailing how the performance of AI models is intrinsically linked to the quality of molecular representation. We elaborate on representations ranging from robust string-based methods to advanced topological models, including the five key categories of Graph Neural Networks (GNNs), three-dimensional (3D)-aware Geometric Deep Learning (GDL) and emerging Quantum Machine Learning (QML) as well as Hybrid Quantum-Classical Neural Networks (HQNNs). We analyzed the practical application of these models across the drug discovery pipeline, including de novo molecular design with biological foundation models and flow matching generative architectures, data scarcity solutions via Few-Shot Learning and meta-learning, and explainable AI (XAI) for transparent validation. We propose an integrated Q-BioFusion framework that synergizes quantum computing, autonomous experimentation, and generative models to address systemic R&D constraints. We hope future research will improve the geometric fidelity to achieve more accurate and faster 3D molecular prediction and generation, enhance data efficiency, and solve the inherent data sparsity problem in biological assays, and advance integrated XAI workflows. These efforts will ensure transparent, reliable and trustworthy guidance during the computer simulation process of drug design.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1776111</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1776111</link>
        <title><![CDATA[Unveiling the thioredoxin fold: a systematic review and bioinformatic analysis of protein disulfide isomerase and Dsb family proteins]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Daniel Cuevas Ortiz</author><author>Karen Werner</author><author>Maria Fernanda Frias Mayo</author><author>Pablo A. Cárdenas Arredondo</author><author>Gabriela F. García Manzano</author><author>Cristina Revilla-Monsalve</author><author>Héctor Retana</author><author>Nelly F. Altamirano-Bustamante</author><author>Myriam M. Altamirano-Bustamante</author>
        <description><![CDATA[IntroductionProtein Disulfide Isomerases (PDIs) and bacterial Dsb proteins are key members of the thioredoxin-fold superfamily, essential for oxidative protein folding in eukaryotic and prokaryotic systems, respectively. Despite their differences in cellular context, these proteins share a conserved thioredoxin domain architecture that enables catalysis of disulfide bond formation, isomerization, and reduction. This systematic review integrates biochemical, structural, and bioinformatic data to identify conserved features within the PDI and Dsb families that underline their catalytic functions.MethodsUsing a PRISMA-based methodology, we screened and analyzed 96 relevant articles and conducted a comparative structural analysis of 11 representative PDI proteins, most of which lack experimentally resolved structures. We leveraged AlphaFold models alongside crystal structures of canonical PDI (PDIA1), DsbC, and DsbG.ResultsWe reveal conserved tertiary folds, catalytic motifs, and domain arrangements across species. These findings highlight the evolutionary conservation and structural versatility of thioredoxin-fold enzymes and underscore their biomedical relevance in diseases linked to protein misfolding, such as neurodegeneration, cancer, and infection.DiscussionThe results offer a foundation for future experimental studies and therapeutic exploration targeting redox-regulating thioredoxin-fold proteins.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1751616</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1751616</link>
        <title><![CDATA[Development and validation of a pipeline for the systematic search for new HLA alleles in WGS data]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Eugene Albert</author><author>Andrei Deviatkin</author><author>Daria Smirnova</author><author>Mary Woroncow</author><author>Gauhar Zobkova</author><author>Olga Mityaeva</author><author>Anna Smirnova</author><author>Viktor Bogdanov</author><author>Pavel Volchkov</author>
        <description><![CDATA[BackgroundHuman leukocyte antigen (HLA) is a highly polymorphic locus in the human genome that also has high clinical significance. New alleles of HLA genes are constantly being discovered but mostly by HLA typing laboratories using field-specific protocols, such as enrichment of the HLA region in high-throughput sequencing data. Nevertheless, a vast amount of whole-genome sequencing (WGS) data has been accumulated over the past years. The main goal of our work was to develop and validate a pipeline specifically tailored toward the identification and characterization of new HLA alleles from 30x WGS sequencing.ResultsIn this article, we present a pipeline, HLAchecker, which is specifically designed to identify potentially new HLA alleles based on discrepancies between predicted HLA types, generated using any other dedicated tool, and the underlying raw 30x WGS data. HLAchecker reports results in a structured way that simplifies further validation of potentially new HLA alleles and streamlines the submission of alleles to appropriate databases. We validated this tool on 4,195 30x WGS samples and 6 HLA genes (A, B, C, DQA1, DQB1, and DRB1) typed by HLA-HD and discovered 17 potentially new HLA alleles with substitutions in exonic regions. We further validated five of these alleles using Sanger sequencing and submitted them to the IPD-IMGT/HLA database.ConclusionHLAchecker is suitable for the identification of new HLA alleles in large WGS cohorts accumulated by the scientific community in recent years. HLAchecker is freely available at https://gitlab.com/EugeneA/hlachecker.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1767362</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1767362</link>
        <title><![CDATA[Machine learning approaches for biomarker discovery using single-cell RNA sequencing]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Gabriel Dewa</author><author>C. Mee Ling Munier</author><author>Sara Ballouz</author><author>Raymond Louie</author>
        <description><![CDATA[The application of single-cell RNA sequencing (scRNA-seq) for biomarker discovery promises unprecedented resolution in identifying potential biomarkers by capturing and analysing cellular heterogeneity. Traditionally, biomarker discovery efforts within single-cell transcriptomics have primarily relied on conventional statistical approaches, particularly through the application of differential gene expression analysis, to identify candidate biomarkers. However, in recent years, with the rapid advancement and growing popularity of artificial intelligence and machine learning, their application in scRNA-seq biomarker discovery has become increasingly prominent. Currently, machine learning-based approaches for scRNA-seq biomarker discovery exhibit considerable methodological diversity, which can be distinguished by factors such as the level of discovery, choice of supervised learning algorithm, feature selection methods, classification metrics, and downstream biological analyses. This review provides a comprehensive overview of the current landscape of machine learning methods for scRNA-seq biomarker discovery, offering researchers a complete and detailed understanding of the field.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1796216</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1796216</link>
        <title><![CDATA[Investigating the molecular mechanisms of the “Tianma-Gouteng” herb pair in treating Parkinson’s disease: a bioinformatics approach and density functional theory with molecular dynamics simulations validation]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liping Zhou</author><author>Chenyang Fei</author><author>Quanxia Liu</author>
        <description><![CDATA[Parkinson’s disease (PD) is a complex neurodegenerative disorder for which current treatments are often symptomatic and lack disease-modifying effects. The traditional Chinese medicine herb pair Tianma-Gouteng, composed of Gastrodia elata Bl (Tianma) and Uncaria rhynchophylla(Miq.) Miq. ex Havil. (Gouteng), has demonstrated clinical efficacy in treating PD motor symptoms, yet its multi-target mechanisms remain unclear. This study employs an integrated approach combining bioinformatics and computational chemistry to elucidate these mechanisms and identify key active components. Methods involved network pharmacology to identify active compounds and PD-related targets, followed by protein-protein interaction network analysis and functional enrichment. Molecular docking and 100-ns molecular dynamics (MD) simulations were utilized to evaluate the binding stability and dynamics of core component-target complexes. Additionally, Density Functional Theory (DFT) was conducted to analyze the electronic properties and reactivity of key compounds. Network pharmacology analysis identified 42 active components and 261 PD-related targets. Core targets identified were AKT1, TP53, and STAT3, which are involved in the regulation of PI3K-AKT signaling, mitochondrial apoptosis, and neuroinflammation. MD simulations demonstrated that quercetin (QU) and kaempferol (KA) formed highly stable complexes with AKT1 and TP53, exhibiting low average root-mean-square deviation (RMSD <0.2 nm), stable radius of gyration (Rg fluctuation <0.05 nm), and sustained protein-ligand hydrogen bonds. In contrast, complexes with 4–4′-hydroxybenzyloxy and 20-hexadecanoylingenol showed conformational instability, consistent with higher entropy penalties. DFT calculations revealed that QU and KA possess low HOMO-LUMO gaps, indicating high chemical reactivity, along with strong nucleophilic regions and intramolecular hydrogen bonds that facilitate target binding. The Tianma-Gouteng pair exerts anti-PD effects through the synergistic modulation of AKT1-mediated PI3K-AKT signaling, STAT3-driven neuroinflammation, and TP53-regulated apoptosis. Quercetin and kaempferol are identified as pivotal components due to their stable target binding and favorable electronic properties, providing a promising foundation for the development of novel PD therapeutics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1771574</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1771574</link>
        <title><![CDATA[Automated deep learning based detection of cellular deposits on clinically used ECMO membrane lungs]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Daniel Pointner</author><author>Michael Kranz</author><author>Maria Stella Wagner</author><author>Moritz Haus</author><author>Karla Lehle</author><author>Lars Krenkel</author>
        <description><![CDATA[IntroductionDespite the promising application of extracorporeal membrane oxygenation (ECMO) in the treatment of critically ill patients, coagulation-associated technical complications, primarily clot formation and critical bleeding, remain a major challenge during ECMO therapy. The deposition of nucleated cells on the surface has been shown, yet the role of these cells towards complication development is still matter of ongoing research. In particular, the membrane lung (MemL) is prone to clot formation. Therefore, the investigation of nuclear deposits on its hollow-fibers may provide insights for a better understanding of the cellular mechanisms involved in the development of ECMO complications.MethodsTo support current research, this study aimed to develop a deep learning–based tool for the automated detection and quantitative analysis of nuclear depositions on MemL hollow-fiber mats. A customized fluorescence microscopy workflow, combined with a semi-automated iterative labeling strategy, was used to generate a high-quality dataset for model training.ResultsSix configurations of instance segmentation models were evaluated, with a Mask R-CNN with ResNet 101 backbone using dilated convolution providing the most balanced performance in both nuclei count and area accuracy. Compared with U-Net–based approaches such as Cellpose or StarDist, the proposed model demonstrated superior segmentation of overlapping and low-intensity nuclei, maintaining accuracy even in densely packed cellular regions.DiscussionWe present an automated image analysis tool for clinically used MemLs, which exhibit complex three-dimensional hollow-fiber architectures and irregular cellular deposits that challenge conventional tools. A dedicated graphical user interface enables streamlined detection, morphometric analysis, and spatial clustering of nuclei, establishing a reproducible workflow for high-throughput analysis of fluorescence microscopy images. This approach eliminates labor-intensive manual counting and facilitates large-scale studies on cell-fiber interactions and disease-related correlations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1777858</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1777858</link>
        <title><![CDATA[Targeting KasA: isosakuranetin derivatives as promising scaffolds for novel anti-tuberculosis agents against drug-resistant Mycobacterium tuberculosis]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>B. Angitha</author><author>T. Amritha</author><author>Radul R. Dev</author><author>Rajesh Raju</author><author>C. V. Umesh</author><author>J. Abhithaj</author>
        <description><![CDATA[IntroductionMycobacterium tuberculosis remains a major global health threat due to the rising prevalence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, which limit the effectiveness of current therapies. The ß-keto-acyl carrier protein synthase (KasA), a key enzyme in the FAS-II pathway for mycolic acid biosynthesis, is a promising target for new anti-tuberculosis agents. Chromolaena odorata, a medicinal plant with reported antimicrobial and antituberculosis activity, is a rich source of bioactive flavonoids, including Isosakuranetin, which shows moderate anti-tuberculosis activity. Modifications in pharmacophores—such as functional groups, structural features, bond angles, and bond distances—can enhance the activity of these phytochemicals and improve their potential as drug leads.MethodologyA structure-based computational workflow was employed, including molecular docking, MM-GBSA binding energy calculations, ADMET evaluation, and 250 ns molecular dynamics simulations to investigate the binding affinity, stability, and pharmacokinetic profiles of Isosakuranetin and its derivatives against KasA.ResultsThe analysis revealed differential binding affinities and dynamic stabilities of Isosakuranetin derivatives. Isn_96 exhibited the strongest binding affinity (−7.921 kcal/mol), with favorable electrostatic and hydrophobic interactions involving residues HIS311, HIS345, and ASP273. Post-MDS MM-GBSA analysis confirmed its enhanced stability, displaying the highest binding free energy (−56.20 ± 6.90 kcal/mol). Pharmacokinetic predictions also indicated acceptable absorption and safety profiles.DiscussionThese findings suggest that Isosakuranetin derivatives, particularly Isn_96, are promising scaffolds for the design of novel KasA inhibitors. Their strong binding affinity, dynamic stability, and favorable ADMET properties highlight potential efficacy against drug-resistant M. tuberculosis. The results emphasize the potential of plant-derived flavonoids as lead compounds and underscore the value of structure-based computational approaches in guiding anti-tuberculosis drug development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1822029</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1822029</link>
        <title><![CDATA[Integrated transcriptomics and machine learning reveal diagnostic biomarkers and immune–stromal remodeling in ischemic heart failure]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yang Sun</author><author>Yu Chang</author><author>Yezhi Feng</author><author>Binghui Song</author><author>Yanan Zhou</author>
        <description><![CDATA[BackgroundIschemic heart failure (IHF) is a major cause of cardiovascular morbidity worldwide, characterized by complex tissue remodeling and inflammation. However, reliable molecular biomarkers for early diagnosis and a systematic understanding of the associated immune–stromal microenvironment remain limited. Identifying specific transcriptomic signatures may enhance diagnostic precision and reveal novel therapeutic targets.MethodsAn integrative transcriptomic analysis was performed utilizing IHF datasets from the Gene Expression Omnibus (GEO). Differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were employed to identify key disease-associated modules. To construct a robust diagnostic model, candidate features were screened using the intersection of four complementary machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and eXtreme Gradient Boosting (XGBoost). The immune and stromal landscape of IHF was comprehensively characterized using a hybrid approach combining MCP-counter and ssGSEA algorithms to quantify cell-type–specific infiltration patterns.ResultsThrough the integration of machine learning strategies, a robust 6-gene diagnostic signature was identified, comprising FCN3, OGN, ITPK1, HMOX2, MTCH1, and HMGN2. Immune deconvolution analysis revealed pronounced remodeling of the IHF microenvironment, characterized by significantly elevated infiltration of Endothelial cells, Macrophages, Neutrophils, and Natural killer cells, indicating a pro-inflammatory and angiogenic phenotype.ConclusionThis study identifies a novel and robust 6-gene diagnostic signature for Ischemic heart failure through a multi-algorithm machine learning framework. These biomarkers are intrinsically linked to pathological alterations in the cardiac stromal and immune microenvironment, particularly fibrosis and innate immune activation. Our findings provide a systems-level view of IHF pathogenesis and offer potential molecular targets for improved diagnosis and therapeutic intervention.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756459</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756459</link>
        <title><![CDATA[molIEreVIS: exploring and interpreting the evidence behind drug repurposing predictions]]></title>
        <pubdate>2026-03-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amal Alnouri</author><author>Andreas Hinterreiter</author><author>Christian Steinparz</author><author>Labinot Bajraktari</author><author>Sebastian Burgstaller-Muehlbacher</author><author>Markus Bauer</author><author>Gregorio Alanis-Lobato</author><author>Marc Streit</author>
        <description><![CDATA[IntroductionFinding new uses for existing drugs, known as drug repurposing, is a widely adopted drug development strategy in the pharmaceutical industry. Computational drug repurposing leverages vast biomedical data to prioritize repurposing candidates. Once these candidates are prioritized, domain experts face the burden of evaluating their true potential.MethodsIn this work, we propose a visualization-based approach to address this challenge for a multimodal class of computational drug repurposing, where heterogeneous evidence modalities are integrated. We conducted a design study in close collaboration with domain experts, from which we derived a domain abstraction of the expert assessment process. Grounded in this abstraction, we developed an interactive visualization approach that explicitly models the expert reasoning process. We applied the proposed approach to create a prototype implementation, molIEreVIS, in the context of an operational drug repurposing pipeline. We used this prototype to collect qualitative feedback from domain experts actively engaged in assessing computational drug repurposing candidates.ResultsThe results demonstrate the potential of our approach to support insights and reasoning in this process and reveal directions for enhancements and future work.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1719700</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1719700</link>
        <title><![CDATA[Boolean network and meshless simulations for the comparison of transport and reaction mechanisms arising in one-short tri-exponential and uniform infusion electrochemotherapeutic treatments]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fabián Mauricio Vélez Salazar</author><author>Iván David Patiño Arcila</author><author>Ismael E. Rivera Madrid</author><author>Marlon Rincón Fulla</author>
        <description><![CDATA[Drug administration via the bloodstream involves some transport and reaction mechanisms (RTMs), such as extravasation, perfusion along blood vessels, transmembrane and interstitial transport, protein dissociation and association, and lymphatic drainage. These RTMs can be influenced by the type of pharmacokinetic (PK) profile used for drug delivery in the circulatory system, as well as by the bloodstream velocity λinl. In electroporated tissues, the electric field magnitude (E) can also affect the RTMs because it brings about vessel vasoconstriction, cell membrane and vessel wall electro-permeabilization, and changes in tissue porosity. In the present work, in-house computational tools are employed to examine how the combination of E and λinl influences the RTM′s existence, interaction, and rates arising in electrochemotherapy for two different PKs: One-short tri-exponential (TPK), where the drug concentration decreases exponentially after a one-short infusion, and one uniform (UPK), where the drug concentration is kept constant during the whole treatment. First, the ratios between extracellular, free intracellular, and bound intracellular concentrations are obtained from numerical simulations with a meshless code previously developed, calibrated, and validated. Subsequently, the interaction between the RTMs is investigated by means of a Boolean model presented here that is based on the comparison of the spatio-temporal evolution of the concentration ratios. Several combinations of E (0 kV/m; 46 kV/m; 70 kV/m), λinl (1×10−4m/s; 1×10−3m/s; 1×10−2m/s), and PK (TPK and UPK) are tested. The in silico findings indicate that RTM′s existence, interaction, and rates can vary between the two PKs (TPK and UPK) for a specific permutation of E and λinl. Nevertheless, common features are identified between these pharmacokinetic profiles. In general, the lower E, the more uniform the transmembrane transport in the radial and axial direction; the decrease of λinl also improves the radial homogeneity of this transport mechanism but negatively influences the axial uniformity. The uniformity of the mechanisms of association and dissociation is only altered monotonously by E (Vélez Salazar and Patiño Arcila, 2025).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1793862</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1793862</link>
        <title><![CDATA[Human-associated NDM-5-producing multidrug-resistant Escherichia coli detected in retail beef and pork in Hungary, 2021]]></title>
        <pubdate>2026-03-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mirena Ivanova</author><author>Joana Mourão</author><author>Judit Szarvas</author><author>Elif S. Tosun</author><author>Niamh Lacy-Roberts</author><author>Natasia Rebekka Thornval</author><author>Zita Záborcki</author><author>Szilárd Jánosi</author><author>Raquel Garcia-Fierro</author><author>Pierre-Alexandre Beloeil</author><author>Ernesto Liebana</author><author>Beatriz Guerra</author><author>Rene S. Hendriksen</author><author>Jette S. Kjeldgaard</author>
        <description><![CDATA[BackgroundCarbapenem-resistant Enterobacterales pose a significant public health threat, particularly when detected in food-producing animals and retail meat. Although carbapenems are not used in European Union animal production, sporadic cases of carbapenemase-producing Escherichia coli have emerged across multiple European countries since 2019. The detection of human-associated carbapenemase genes in meat raises concerns about potential transmission to humans through the food chain.MethodsIn this study, we characterize three multidrug-resistant (MDR) E. coli isolates harboring blaNDM-5 recovered from retail beef and pork in Hungary in 2021. E. coli isolates were subjected to phenotypic antimicrobial susceptibility testing using broth microdilution, conjugation experiments, and genotypic characterization through whole-genome sequencing using Illumina and Oxford Nanopore platforms. Hybrid assemblies enabled comprehensive comparative genomic and plasmid analyses.ResultsAll three isolates belonged to the human-associated uropathogenic clone ST405 (O102:H6) and were clonally related with a maximum of two single nucleotide polymorphisms. They exhibited identical genomic profiles conferring resistance to carbapenems, cephalosporins, fluoroquinolones, tetracycline, and azithromycin. Comparative genomic analysis revealed close genetic relationships with human clinical isolates from Australia and the United Kingdom, suggesting international dissemination. The blaNDM-5 gene was located on conjugative IncFII-IncFIB hybrid plasmids (approximately 132 kb) closely related to clinical plasmids from human isolates in the United States, differing only by the absence of a blaCTX-M-15-ISEcp1 transposition unit.ConclusionThe detection of human-associated blaNDM-5-carrying E. coli ST405 in retail meat represents a serious food safety concern, highlighting potential transmission routes to humans and emphasizing the need for enhanced surveillance and epidemiological investigations.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756507</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756507</link>
        <title><![CDATA[SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christian A. Steinparz</author><author>Andreas Hinterreiter</author><author>Labinot Bajraktari</author><author>Vitaly Sedlyarov</author><author>Markus J. Bauer</author><author>Thomas Zichner</author><author>Marc Streit</author>
        <description><![CDATA[IntroductionValidating large knowledge graphs with the Shapes Constraint Language (SHACL) often yields violation reports too large to interpret and trace to root causes, especially in industry-scale datasets such as pharmaceutical omics pipelines.MethodsWe present SHACLens, an interactive visualization workflow—developed with a major pharmaceutical partner—that links ontology, instance data, and violation reports across multiple coordinated views. We contribute a practitioner-informed workflow co-designed with pharmaceutical data-analysis experts. A Node-Link View combines ontology and groups of equivalent violations, a projection view reveals clusters of nodes with similar errors, a LineUp table combines instance data with violation information, a Class Tree offers a class-hierarchy overview, and an integrated LLM assistant provides contextual explanations and can operate the system via natural-language commands.ResultsWithin this workflow, selections and filters propagate across views, exposing co-occurring errors and their likely upstream causes. Analysts iteratively identify violation clusters, inspect correlations, and trace the detailed cause of errors.Evaluation and implicationsWe evaluated SHACLens through an iterative expert-in-the-loop design process with the partner team and a qualitative study on a transcriptomics dataset containing 5,203 violating nodes with the same experts. In this study, SHACLens efficiently surfaced repeated sets of errors due to missing objects and schema inconsistencies, supporting goal-oriented analysis and serendipitous findings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1711637</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1711637</link>
        <title><![CDATA[Machine learning identifies molecular targets of Di (2-ethylhexyl) phthalate in pulmonary arterial hypertension]]></title>
        <pubdate>2026-03-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hua Li</author><author>Yingchun Jiang</author><author>Jijia Li</author>
        <description><![CDATA[ObjectiveThis study aims to explore the potential molecular mechanisms by which di (2-ethylhexyl) phthalate (DEHP) exposure induces pulmonary arterial hypertension (PAH).MethodsWe conducted differential expression analysis on multiple genomics datasets to pinpoint PAH-associated genes. Subsequently, an integrative approach combining machine learning algorithms and network toxicology was employed to examine the binding interactions between DEHP and the identified target proteins.ResultsOur analysis identified 60 genes as potential targets of DEHP in PAH. Further refinement using machine learning prioritized twelve core regulatory genes: ALKBH2, AOC2,BCL2L10,CTBP2,DNM2,ERLIN2,HPS6,RABGGTA,PON2,SLC4A7,SORT1, and PDE4D. Among these, HPS6, CTBP2,RABGGTA, SORT1,ALKBH2,BCL2L10, AOC2,and PON2 were significantly downregulated, whereas SLC4A7,PDE4D, ERLIN2,and DNM2 were markedly upregulated (P < 0.05).ConclusionThese findings demonstrate that DEHP promotes PAH pathogenesis by modulating specific genes and associated pathways. The twelve core genes identified through machine learning are proposed as key regulators in this process, providing crucial insights for future mechanistic investigation into DEHP-induced PAH.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1755412</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1755412</link>
        <title><![CDATA[Generative AI in drug repurposing and biomarker discovery: a multimodal approach]]></title>
        <pubdate>2026-03-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>K. Saranya</author><author>Emerson Raja Joseph</author><author>Ts. Kalaiarasi</author><author>M. Karthiga</author>
        <description><![CDATA[IntroductionComputational drug repurposing has been widely explored using similarity-based methods, network diffusion, matrix factorization, deep learning, and graph neural networks (GNNs). However, recent heterogeneous GNN models, such as TxGNN and GAT-based models, demonstrate serious limitations for real-world biomedical applications, including poor generalization to sparsely annotated diseases, limited disease-level adaptation, and inability to effectively combine heterogeneous evidence from curated databases, multi-omics profiles, and unstructured biomedical literature.MethodsThis article proposes a heterogeneous attention-based meta-learning graph neural network named HAMGNN, which employs three major innovations: (i) relation-sensitive multi-head attention to prioritize biologically significant interactions among heterogeneous edge types, (ii) a disease-focused meta-learning framework enabling rapid adaptation to newly observed or under-informed diseases, and (iii) a literature-enhanced knowledge graph construction pipeline encoding high-confidence, LLM-extracted therapeutic information. The model was tested on a large multimodal biomedical knowledge graph assembled from DrugBank, DisGeNET, and Hetionet, comprising more than 2.2 million edges, using a stringent disjoint disease-based (cold-start) evaluation protocol.ResultsHAMGNN achieved a receiver operating characteristic–area under the curve (ROC–AUC) of 0.98 and precision of 0.95, representing a 10%–15% improvement over TxGNN and GAT-GNN on unseen disease generalization. Translational applicability was demonstrated through Alzheimer’s disease and Long COVID case studies, identifying clinically plausible repurposing candidates and disease-associated biomarker signatures via mechanistic pathways.DiscussionHAMGNN offers a generalized, biologically grounded, and unified framework for evidence-based drug repurposing and biomarker discovery in complex and emerging diseases.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1826409</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1826409</link>
        <title><![CDATA[Correction: Computational discovery of SARS-CoV-2 viral entry inhibitory peptides from Androctonus mauretanicus scorpion venom: molecular docking and molecular dynamics simulations targeting the spike protein]]></title>
        <pubdate>2026-03-19T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Reda Chahir</author><author>Salaheddine Redouane</author><author>Jacob Galan</author><author>Hicham Hboub</author><author>Lahoussaine Aserrar</author><author>Salma Chakir</author><author>Ahmed Salim Lahlou</author><author>Hinde Aassila</author><author>Rachid El Fatimy</author><author>Naoual Oukkache</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1770550</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1770550</link>
        <title><![CDATA[Japanese genomes for pharmacogenomics: primary and secondary pipelines for population-specific insights]]></title>
        <pubdate>2026-03-19T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Charles W. Crawford</author><author>Yuka Nakano</author><author>Marika Hayashi</author><author>Antony Ibrahim</author><author>Issei Kono</author><author>Iri Sato-Baran</author><author>Takeshi Ozeki</author>
        <description><![CDATA[Pharmacogenomics (PGx) incorporates population level allele frequencies into its analyses. Often these population groupings are based on current or ancestral geographic location. However, these groupings can obscure internal variance caused by population heterogeneity. In order to increase the data accuracy and specificity for researchers, it is necessary to refine the population groupings. Often the necessary datasets have already been collected but have not been fully analyzed past their initial purpose. Here we provide a secondary pipeline that demonstrates a divergence between two datasets: the Clinical Pharmacogenetics Implementation Consortium (CPIC) collection of geographic populations and a Whole Genome Sequencing PGx dataset consisting of 632 Japanese individuals. Three classes of drugs and the relevant genes as identified by CPIC are examined: SSRIs (CYP2D6, CYP2B6, CYP2C19), opioid analgesics (CYP2D6) and statins (SLCO1B1). A meaningful divergence is shown between CPIC’s East Asian population and the Japanese population for opioid analgesics and statins. For opioid analgesics the Japanese population saw an increase in the “Use as directed” designation compared to the East Asian population from 53.2% to 71.0%; the statins data showed a decrease from 75.7% in the East Asian population 67.6% in the Japanese population. This divergence demonstrates that existing WGS data can reveal PGx patterns masked by broad geographic groups through the application of an appropriate secondary pipeline, enabling 0population specific implementation and refined population-level PGx inference without the need for further sample collection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1779654</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1779654</link>
        <title><![CDATA[Designing and immuno-informatics evaluation of a multi-epitope vaccine targeting lipoprotein A-4′-phosphatase (LpxF) for Helicobacter pylori infection control]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pavan Gollapalli</author><author>Tamizh Selvan Gnanasekaran</author>
        <description><![CDATA[IntroductionThe WHO has classified Helicobacter pylori as a category 1 carcinogen and a major causative agent of gastrointestinal ulcers, gastric adenocarcinoma, and gastric lymphoma. While antibiotics and proton pump inhibitors are effective treatments, they are associated with risks of reinfection, patient dissatisfaction, and increasing antibiotic resistance. Due to the bacterium's extremophile nature, designing potent drugs remains challenging. Therefore, an effective vaccine represents the most suitable prophylactic option for mass administration.MethodsA subtractive proteomics pipeline was employed to identify appropriate antigenic proteins for the development of a multi-epitope vaccine (MEV). Lipid A-4'phosphatase (LpxF) was selected as a potential target. Various bioinformatics and immunoinformatics databases were used to predict T and B cell epitopes. A 757 amino acid MEV was then constructed by combining eight cytotoxic T cell (CTL), nineteen helper T cell (HTL), and fourteen linear B cell (LBL) epitopes using appropriate adjuvants and linkers. The vaccine's interaction with human immunological receptors (TLR2, TLR4, and TLR5) was evaluated via molecular docking and molecular dynamics (MD) simulations. Finally, the pET-28a(+) plasmid vector from Escherichia coli was used to assess expression capabilities.ResultsThe proposed MEV was found to be non-allergic, stable, and highly antigenic for human use. Computational simulations, including molecular docking and MD, demonstrated strong binding affinity and stable molecular interactions between the MEV and target immune receptors. In silico cloning results further confirmed the expression potential of the vaccine within the E. coli system.DiscussionBased on these computational findings, the designed MEV shows significant promise for establishing protective immunity against H. pylori. The multi-epitope approach addresses the challenges posed by the bacterium's resilient nature. However, while the in silico results are encouraging, further in vitro and in vivo investigations are required to fully comprehend and validate its immune-protective efficacy in biological systems.]]></description>
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