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        <title>Frontiers in Genetics | Computational Genomics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/genetics/sections/computational-genomics</link>
        <description>RSS Feed for Computational Genomics section in the Frontiers in Genetics journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-08-17T16:54:01.946+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1813369</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1813369</link>
        <title><![CDATA[Integrating single-cell transcriptomics with deep learning for glioblastoma treatment]]></title>
        <pubdate>2026-08-17T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Marybeth G. Yonk</author><author>Mainak Mustafi</author><author>Megan A. Lim</author><author>Kimberly B. Hoang</author><author>William H. Delaney</author><author>Charee M. Thompson</author><author>Thais Federici</author><author>Yuhong Du</author><author>Nicholas M. Boulis</author><author>Antonina Mitrofanova</author><author>Kecheng Lei</author>
        <description><![CDATA[Glioblastomas are aggressive, heterogeneous tumors that present significant challenges in both diagnosis and treatment. Despite advances in surgical resection, radiotherapy, and chemotherapy with temozolomide (TMZ), the prognosis for glioblastoma patients remains poor, largely due to tumor heterogeneity and resistance mechanisms, such as genetic mutations in DNA repair pathways. To address these specific heterogenous qualities of glioblastoma, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for characterizing glioblastoma tumors, enabling the identification of subpopulations that respond differently to treatment. However, utilizing the vast amount of data generated by scRNA-seq poses challenges in clinical applications. To overcome this challenge, computational models have been introduced to more effectively process patient scRNA-seq data into more digestible information for clinicians. More specifically, advanced deep learning approaches show promise for processing and analyzing patient scRNA-seq data, enhancing informed treatment approaches for highly heterogenous glioblastoma. This review aims to explain how scRNA-seq can be used to identify important areas of glioblastoma treatment resistance, evaluate current glioblastoma scRNA-seq-based deep learning models, and outline relevant training datasets to overcome patient scRNA-seq data availability limitations. Ultimately, these deep learning models can be utilized by researchers and clinicians to provide more informed and precise treatment to glioblastoma patients.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1799530</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1799530</link>
        <title><![CDATA[Sex-dependent prediction of autism]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Catriona J. Miller</author><author>Theo Portlock</author><author>Denis M. Nyaga</author><author>Justin M. O’Sullivan</author>
        <description><![CDATA[IntroductionAutism spectrum disorders (ASD) have a global prevalence of 1%, with a male-to- female diagnosis ratio of roughly 4:1. Several models have been developed to predict ASD using genetic information. However, the influence of biological sex on prediction outcomes remains underexplored.MethodsWe present an ensemble model to predict ASD, which integrates polygenic risk scores (PRSs), common genetic variants, and ASD risk genes with the MSSNG whole genome sequencing (WGS) dataset.ResultsFollowing training, our model achieved an accuracy of 0.68, an area under the receiver operating curve (AUROC) of 0.72, and a recall of 0.77 on the test dataset. Notably, common variants contributed more significantly to ASD prediction in males than females (p < 0.001), with accuracies of 0.69 and 0.66, respectively. The 16p11 locus emerged as particularly predictive for females (p < 0.001). Gene enrichment analysis using the Allen Brain Atlas revealed that expression of ASD risk genes that were significant in females were enriched (FWER < 0.05) in the primary somatosensory cortex, inferior parietal cortex, and parietal neocortex during fetal development. By contrast, male ASD risk gene expression was enriched (FWER < 0.05) in the dorsolateral prefrontal cortex and anterior cingulate cortex across developmental stages (fetal to adult).DiscussionThese findings underscore a sex-dependent role for common genetic variants in the risk of developing ASD. In doing so, they highlight the utility of ensemble models that incorporate common variation and biological sex for ASD prediction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1913487</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1913487</link>
        <title><![CDATA[PySimi: a unified framework for similarity measure evaluation in spectral clustering with applications to omics data]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tianyi Shi</author><author>Xiucai Ye</author><author>Zeng Zou</author><author>Wenyu Xi</author><author>Tetsuya Sakurai</author>
        <description><![CDATA[High-throughput omics technologies generate increasingly large and complex datasets, creating a growing demand for clustering methods capable of identifying meaningful biological patterns. Spectral clustering is widely used for analyzing high-dimensional omics data, but its performance strongly depends on the construction of the similarity matrix. Although numerous similarity measures have been proposed, most existing spectral clustering tools support only a limited set of similarity construction strategies, making systematic evaluation and comparison difficult. Here, we present PySimi, an open-source Python framework for flexible similarity matrix construction and spectral clustering. PySimi integrates classical, adaptive, and neighborhood‐based similarity measures within a unified and extensible framework and provides a consistent workflow for constructing, comparing, and evaluating similarity matrices. The framework also supports downstream analyses, including dimensionality reduction and visualization, and offers an interactive web application for exploratory analysis. We evaluated PySimi using multiple bulk and single-cell RNA-sequencing datasets. The results demonstrate that the choice of similarity measure can substantially influence clustering outcomes and downstream biological interpretation. While no single method consistently achieved the best performance across all datasets, adaptive and neighborhood-based approaches generally showed stronger performance than classical methods. By providing a unified platform for similarity matrix construction, comparison, and evaluation, PySimi enables systematic investigation of similarity measures and facilitates their application to diverse omics datasets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1880490</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1880490</link>
        <title><![CDATA[OtoVCE: a mechanism-aware language-model evidence layer for hereditary hearing-loss variant interpretation]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shaopei Ye</author><author>Lan Wang</author><author>Peng Chen</author>
        <description><![CDATA[BackgroundMissense variants in genes implicated in hereditary hearing loss are frequently returned to clinicians as variants of uncertain significance. In silico predictors and rule-based ACMG/AMP frameworks each capture only a fraction of the required evidence, and neither directly accesses case-level and functional evidence reported in the published literature.MethodsWe developed OtoVCE, a four-stage framework that places a large language model inside a calibrated ACMG/AMP rule engine as a structured evidence extractor rather than an end-to-end classifier. Rule-encodable evidence—six in silico missense predictors, gnomAD allele frequencies, UniProt domain annotations, and a hearing-loss-specific protein language model with a pathogenicity head and a mechanism head—is aggregated by the ClinGen Hearing Loss VCEP rule set. For variants that remain uncertain, OtoVCE retrieves the literature from five sources and prompts the language model using the Brnich functional evidence rubric, with the predicted disease mechanism as context, returning PS3, BS3, and PS4 strength assignments with PubMed-verified citations. Rule-based and language-model-derived strengths are then combined into a posterior probability mapped to the five ACMG/AMP classes.ResultsOn a held-out post-2024 ClinVar cohort (N = 1,885), OtoVCE achieved an area under the receiver-operating curve (AUC) of 0.997 and a sensitivity of 93.6% at a 1% false-positive rate. Performance was preserved across an OTOF gene-leave-out cohort (AUC = 0.993), the external Deafness Variation Database (AUC = 0.917), and the protein-language-model training cohort itself, with model-derived rules disabled (AUC = 0.976), excluding training-set memorization. A paired A/B comparison (N = 1,930) attributed the literature evidence contribution to the mechanism cue: 7.1-fold more cited identifiers, 11.6-fold more quantitative evidence, and diagnostic strength scores (paired-bootstrap ΔAUC +0.18). OtoVCE identified 211 of 2,389 uncertain variants as one piece of supporting evidence short of likely pathogenic at 98.1% precision, agreed with ClinGen-VCEP curation at Cohen’s κ = 0.57, and verified 100% of 3,185 cited PubMed identifiers.ConclusionA literature-grounded, mechanism-aware language-model evidence layer can serve as a complementary component within a calibrated ACMG/AMP workflow for clinical variant interpretation in hereditary hearing loss.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1845510</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1845510</link>
        <title><![CDATA[A reproducible pretreatment mucosal-inflammatory-remodeling state is associated with induction-phase anti-tumor necrosis factor non-response in ulcerative colitis]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hongwei Zheng</author><author>Xin Zhuang</author><author>Wenbiao Chen</author>
        <description><![CDATA[BackgroundPrimary non-response to anti-tumor necrosis factor (anti-TNF) therapy remains a major clinical challenge in ulcerative colitis (UC); however, public-data transcriptomic studies often yield unstable single-gene biomarkers and limited replication.MethodsWe analyzed three independent pretreatment UC mucosal transcriptome cohorts from infliximab-treated patients (total n = 79). The induction response was assessed at 4–8 weeks according to the original definition in each study. To prioritize cross-cohort biology over single-probe overlap, we integrated recurrent effect direction, pathway convergence, and sample-level scoring. We defined a 68-gene consensus signature for interpretation, evaluated generalization with leave-one-dataset-out (LODO) scoring, tested attenuation after adjusting for baseline inflammatory/remodeling proxies, and examined cellular localizations in public epithelial (GSE116222) and rectal immune (GSE125527) single-cell references.ResultsThe 68-gene consensus signature was higher in non-responders across all three cohorts. In the LODO analysis, the held-out non-response axis remained elevated in the held-out non-responders, with a pooled random-effects size of Hedges’ g = 1.36 (95% confidence interval: 0.83‐1.89; p = 5.65 × 10−7; I2 = 0%). The pathway analyses consistently implicated inflammatory responses, TNF-α/NF-κB, IL-6–JAK–STAT3, interferon signaling, hypoxia, and epithelial–mesenchymal transition (EMT), whereas oxidative phosphorylation was enriched in the responders. Adjustments for inflammatory proxies with and without EMT attenuated the effects, but the associations remained directionally positive in all cohorts and retained nominal significance in two cohorts under the strictest model. In public single-cell references, the signature activities were enriched in cycling/stem-like and stress-inflammatory epithelial states and were highest in the rectal myeloid/dendritic cells (M/DCs).ConclusionIn the infliximab-treated UC cohorts, induction-phase non-response is associated with reproducible pretreatment mucosal-inflammatory-remodeling states that remain evident in the held-out cross-cohort analyses while localizing most strongly to rectal M/DCs and stress-associated epithelial states in public single-cell references. The signal overlaps substantially with the baseline inflammatory/remodeling burden, but its direction is consistent across the cohorts. These findings support a biologically coherent disease-state framework rather than a deployable clinical predictor.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1922890</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1922890</link>
        <title><![CDATA[Editorial: Methods for imaging and omics data science: advances, applications, and spatiotemporal innovations]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Himel Mallick</author><author>Lingling An</author><author>Shrabanti Chowdhury</author><author>Anchal Ghai</author><author>Suvo Chatterjee</author><author>Siyuan Ma</author><author>Ali Rahnavard</author><author>Pinaki Sarder</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1871376</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1871376</link>
        <title><![CDATA[A novel multivariate framework for functional gene networks enrichment analysis]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Heewon Park</author><author>Seiya Imoto</author>
        <description><![CDATA[Gene network analysis is critically implicated in disease research for uncovering functional modules and interaction-driven pathways underlying biological and disease processes. However, the interpretation of large inferred networks remains challenging. Although functional gene network analysis allows the interpretation of large inferred networks, challenges such as reduction of multiple network-level features to a single composite score often limit their application. This data reduction can mask the important multivariate characteristics of gene networks, hindering efficient differentiation of individual contributions of distinct network components. Hence, this study aimed to investigate a novel computational strategy called Multivariate Framework for Functional Gene Network Enrichment Analysis (mFGNA). This framework incorporated diverse network-level features from a graph-theoretical perspective, including node properties (centrality), edge connectivity patterns (Jaccard distance), interaction strengths (edge weights), alongside traditional expression levels. Notably, mFGNA preserved these multidimensional characteristics, capturing complex rewiring of gene networks across different phenotypic states. Furthermore, mFGNA adopted a gene-level permutation strategy to evaluate the enrichment hypothesis, ensuring effective statistical inference and reduced computational complexity compared with phenotype-based permutations. Extensive Monte Carlo simulations validated mFGNA through both undirected and directed gene networks, showing consistently improved performance over existing approaches across diverse pathway settings. We also applied mFGNA to investigate immune pathway perturbations in cancer cell lines and identified significant network-level dysregulation in pancreatic and non-small cell lung cancers. Cancer-specific interaction modules were dominated by human leukocyte antigen class II genes. Meanwhile, normal cell networks were characterized by hub genes such as MMP1 and MMP3 that were implicated in tissue maintenance, highlighting immune remodeling in tumors and the potential molecular targets for developing diagnostic and therapeutic interventions. Overall, the study shows that mFGNA enables effective functional pathway discovery in complex gene networks, providing mechanistic insights and potential translational targets in disease contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1874451</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1874451</link>
        <title><![CDATA[Improved prediction of microbial optimal growth temperatures with neural networks and protein language models]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mensur Dlakić</author><author>William P. Inskeep</author>
        <description><![CDATA[IntroductionTemperature is one of the strongest selective forces that determines the composition of microorganisms in the environment. Structural properties of proteins shape the thermal adaptation of an organism at the macro level, and aggregate protein features can be used for many types of predictions. Because most microorganisms remain uncultured, the inference of physiological traits, such as optimal growth temperature, has become essential for microbial ecology and biotechnology.MethodsA large dataset of optimal growth temperatures for microorganisms was compiled from the literature and used to train several machine learning predictors. Our goal was also to test the usefulness of protein language models and to evaluate predictive performance on incomplete genomes.ResultsWe confirmed a strong correlation between protein sequence properties and optimal growth temperatures. The analysis showed that calculating better protein sequence features, specifically through protein language models, leads to more accurate predictions.DiscussionWe used state-of-the-art tools and compared our models with several others developed for optimal growth temperature prediction over the past 2 decades. Our models showed excellent ability to generalize across a range of temperatures. We concluded that larger datasets and increased representation of psychrophiles and thermophiles will be needed to continue improving the predictors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1896629</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1896629</link>
        <title><![CDATA[mwHIT: accelerated and accurate histone modification imputation using multi-scale window attention]]></title>
        <pubdate>2026-07-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhaoxi Zhang</author><author>Lijuan Jia</author><author>Xiaoya Fan</author><author>Zengyou He</author><author>Juan Wang</author><author>Zhong Wang</author>
        <description><![CDATA[Histone modifications regulate chromatin organization and gene activity, and abnormal modification patterns are closely associated with disease-related transcriptional dysregulation. Although ChIP-seq provides accurate measurements of histone modification profiles, genome-wide experimental profiling remains costly and time-consuming. Here, we propose mwHIT, a multi-scale window attention-based Histone Modification Imputation Transformer that predicts histone modification signals from run-on sequencing (RO-seq) and DNA sequence features. mwHIT captures both local and long-range regulatory information while reducing the computational cost of transformer-based inference. Across cell lines and histone marks, mwHIT achieved an average Pearson correlation of 0.7086 in matched method comparisons, outperforming dHIT and Enformer by 13.7% and 33.4%, respectively. On the GM12878 benchmark, mwHIT reduced average mean squared error (MSE) by 69.7% relative to dHIT and 34.6% relative to Enformer. Compared with a full-attention transformer baseline, the multi-scale window attention design reduced runtime from 0.05 h to 0.03 h while maintaining comparable or slightly higher predictive accuracy. mwHIT also recovered histone modification patterns in promoters, enhancers, gene bodies, and regions centered on transcription start sites (TSSs), and highlighted disease-associated regulatory signals near the IL2RA locus. These results demonstrate that mwHIT can support the discovery of disease-associated regulatory markers and provide computational evidence for prioritizing candidate regulatory regions and potential regulatory targets. mwHIT’s data processing methods and model code are freely available in the mwHIT GitHub repository at https://github.com/zhichunlizzx/mwHIT.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1814786</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1814786</link>
        <title><![CDATA[Global–local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kirti Pant</author><author>Pijush Kanti Dutta Pramanik</author><author>Shahid Mohammad Ganie</author><author>Anindita Saha</author><author>Zhongming Zhao</author>
        <description><![CDATA[BackgroundAccurate classification of brain tumors in MRI scans is critical for effective clinical decision making, yet manual assessment is labor-intensive and subject to variability. Although deep learning models, particularly CNNs and Transformers, have shown potential, each architecture alone has limitations: CNNs excel at local feature extraction but lack global context awareness, while Transformers capture global relationships but may overlook fine-grained details.ObjectivesTo develop a hybrid Transfer Learning–Transformer model that integrates CNN-driven local feature modeling with Transformer-based global reasoning to enhance multi-class brain tumor classification.MethodsThe proposed workflow comprises three stages: (1) benchmarking standalone CNN (ResNet50, VGG19, ConvNeXtBase, EfficientNetV2B0) and Transformer models (ViT, Swin, DeiT, PoolFormer); (2) constructing two intra-family hybrids—HTL (ResNet50 + ConvNeXtBase) and HTF (PoolFormer + ViT); and (3) fusing them into a final hybrid model (HF). Two public MRI datasets (Figshare, Kaggle) were used. Models were trained, validated, and tested on the Kaggle dataset, while the Figshare dataset was used exclusively for external validation. Performance was assessed using accuracy, precision, recall, F1-score, AUC, Friedman’s aligned-rank test, Kendall’s W, Holm post hoc analysis, TOPSIS-based ranking, calibration analysis (Brier score), and Grad-CAM++ for interpretability.ResultsThe HF model achieved near-perfect classification (∼99.4% on Kaggle and up to 100% on Figshare), significantly outperforming all baseline and single-architecture models. Statistical and multi-criteria analyses consistently ranked HF as the top-performing model, while calibration results confirmed reliable probability estimation. Grad-CAM++ further indicated tumor-focused decision-making.ConclusionThe proposed hybrid model delivers high accuracy, strong generalizability, and reliable, interpretable predictions across MRI datasets, positioning it as a promising solution for AI-assisted brain tumor diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1850219</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1850219</link>
        <title><![CDATA[CNNKSCEC: a deep learning-based framework for chromatin loop prediction with multi-source feature integration]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Junfeng Wang</author><author>Bingzi Zheng</author><author>Lili Wu</author><author>Xiaoyan Liu</author><author>Haixia Zhai</author><author>Junwei Luo</author>
        <description><![CDATA[MotivationChromatin in the cell nucleus adopts a complex three-dimensional (3D) structure shaped by folding and interactions, with chromatin loops serving as fundamental organizational units. Accurate loop prediction is essential for understanding gene regulation and disease mechanisms. However, existing chromatin loop prediction methods still face challenges in noise handling, data imbalance, and multi-omics integration.ResultsIn this study, we present CNNKSCEC, a deep learning-based framework for chromatin loop prediction via multi-source feature fusion. The model integrates Hi-C and DNase-seq data into a dual-channel feature matrix as input. It employs a three-stage iterative feature extraction framework consisting of a dual-branch convolutional module (CNNC), a SCConv module combining SRU and CRU, and an ECHybridAddition module integrating both ECA and CBAM attention mechanisms. This design enables iterative multi-scale feature extraction and enhances the feature representation capability of the input matrix. Finally, the model uses a fully connected layer for classification, generating candidate chromatin loops with prediction scores, and filters out false candidates through density-based clustering. In the experiments, we compare CNNKSCEC with existing chromatin loop prediction methods, and the results demonstrate that the approach outperforms other methods overall in terms of performance. The code is available from https://github.com/zhengbingzi/CNNKSCEC.git.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1863100</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1863100</link>
        <title><![CDATA[GAHIB: graph attention VAE with a hyperbolic information bottleneck for biologically structured single-cell representations]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zeyu Fu</author><author>Jiawei Fu</author><author>Xiaoxia Wang</author><author>Yiyao Liu</author><author>Tianfei Ran</author>
        <description><![CDATA[Current single-cell RNA-sequencing (scRNA-seq) variational autoencoders (VAEs) usually emphasise local cell graph structure, hyperbolic latent geometry, or bottleneck compression separately, yet these biases are rarely evaluated together in one evidence-gated representation model. We present GAHIB (graph attention VAE with a hyperbolic information bottleneck), which combines a graph-attention encoder, a 2D information bottleneck, and a Lorentz-hyperbolic geometry loss. Because the main clustering benchmark uses Leiden-derived proxy labels rather than definitive biological ground truth, we evaluate the model in two tiers: a broad 53-dataset proxy-label benchmark for method characterisation, and curated-label and marker analyses on annotated systems for biological interpretation. Across the proxy benchmark, GAHIB shows a balanced aggregate profile across clustering, projection-quality, and latent-structure metrics, while important comparisons remain mixed: scVI is statistically close to NMI/ARI, and scDHMap remains competitive on DRE-UMAP. On curated-label systems, the biological signal remains context-dependent; muscle atlas analyses support lineage-aligned structure with marker enrichment, whereas the fine T-cell immune-subtype task favors scVI. Sensitivity, seed-stability, a bounded count-dropout pilot, and cost analyses indicate practical runtime under the tested settings; however, the dropout evidence is limited to named pilot systems, and complete manually curated provenance remains an explicit limitation. Together, the results position GAHIB as a complementary, geometrically aware, single-cell representation rather than a drop-in clustering replacement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1817432</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1817432</link>
        <title><![CDATA[miRNAProtPred: computational prediction of human miRNA binding based on seed complementarity and thermodynamic stability]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Somenath Dutta</author><author>Manisha Pritam</author><author>Sudipta Sardar</author><author>Nitimoy Mondal</author><author>Sun Gu Lee</author>
        <description><![CDATA[IntroductionComputational prediction of microRNA-target interactions is essential for understanding post-transcriptional regulation, yet existing tools often require manual data curation, lack comprehensive miRNA databases, or provide limited guidance for experimental prioritization.MethodsWe developed miRNAProtPred, a Python package that consolidates established seed complementarity matching and ViennaRNA-based thermodynamic analysis into a streamlined workflow for predicting human miRNA binding sites on diverse target sequences. The tool integrates 2,656 curated human miRNAs from miRDB and miRBase, accepts diverse input formats (DNA, RNA, or protein sequences), and classifies predictions through a multi-criteria confidence framework incorporating seed complementarity, thermodynamic stability (minimum free energy, MFE), flanking AU content, motif identity, and match type. miRNAProtPred supports two user-selectable search modes: a default strict mode requiring exact Watson-Crick seed complementarity, and a relaxed mode that extends sensitivity to G:U wobble-supported interactions through a hierarchical exact-first fallback strategy. The tool was evaluated using experimentally validated antiviral miRNAs from SARS-CoV-2 and HIV-1, and independently benchmarked on the miRAW dataset (62,215 miRNA-target pairs).ResultsFor SARS-CoV-2, miRNAProtPred successfully identified all 16 experimentally supported inhibitory miRNAs compiled from multiple independent studies (100% recovery in strict mode), with 12 of 16 (75%) classified as high confidence (MFE ≤ −12 kcal/mol). For HIV-1, 11 of 13 (84.6%) validated miRNAs were identified through canonical seed matching, with 7 of 11 (63.6%) classified as high confidence; the remaining two miRNAs (hsa-miR-92a-3p and hsa-miR-382-5p) were recovered through the wobble-permissive relaxed mode, achieving complete recovery across both viral systems. Large-scale evaluation on the miRAW benchmark dataset confirmed the precision-oriented performance profile, with strict mode achieving 98.66% precision, 73.97% recall, an F1-score of 0.846, and a Matthews correlation coefficient of 0.748. Validated miRNAs showed thermodynamic enrichment compared to genome-wide predictions (SARS-CoV-2: −11.87 vs. −9.695 kcal/mol; HIV-1: −12.13 vs. −11.215 kcal/mol), supporting MFE-based prioritization.DiscussionmiRNAProtPred provides a streamlined, pip-installable tool for predicting human miRNA binding sites on diverse target sequences, facilitating candidate prioritization for experimental validation. The package is freely available at https://github.com/somenath-combio/mirnaprotpred.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1846707</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1846707</link>
        <title><![CDATA[Stereo-cell coupled with single-cell transcriptomics identifies a transitional metabolic state in maturing skeletal muscle myofibers]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Langchao Liang</author><author>Yuxin Gong</author><author>Chaochao Chai</author><author>Shijie Hao</author>
        <description><![CDATA[Identification of skeletal muscle fiber types and metabolic reprogramming are crucial for postnatal muscle maturation, but high-resolution metabolism and spatial heterogeneity of muscle fibers in mid-maturation remain poorly understood. Our study performed single-cell RNA sequencing (scRNA-seq) and single-cell nuclear RNA sequencing (snRNA-seq) on five hind limb muscles from 5-week-old mice, combined with Stereo-cell at single muscle fiber resolution, to elucidate myofiber subtype maturation and its metabolic changes. Integrating scRNA-seq and snRNA-seq data, a comprehensive mouse skeletal muscle cell atlas was constructed, demonstrating the complementary advantages of the two techniques in capturing interstitial cells and multinucleated muscle fibers. Our analysis resolved a continuous maturational lineage from type I to IIB myonuclei (IIB_1–3). Notably, the IIB_3 myonuclei subtype exhibited a dual hypermetabolic phenotype, with increased oxidative phosphorylation (OXPHOS) and glycolytic activity, which differs from the purely glycolytic phenotype observed in adult mice. Stereo-cell further validated this transitional metabolic state and revealed spatial heterogeneity within individual IIB myofibers, with localized high-oxidation regions. Furthermore, we observed a mixed myofiber phenotype, with subtype-specific myosin heavy chain expression enriched at the myofiber terminals, indicating directional transformation of myofiber during maturation. In summary, this study reveals a previously undescribed transitional metabolic feature of IIB-type myofiber during postnatal muscle maturation and elucidates the spatial metabolic heterogeneity of myofiber, providing new insights into the regulatory mechanisms of skeletal muscle developmental plasticity and metabolic specialization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1820192</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1820192</link>
        <title><![CDATA[Transcriptomic profiling and experimental validation of myeloid-cell-differentiation-related key genes in osteoarthritis]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiaming Liu</author><author>Xinyu Zhang</author><author>Hai Xu</author><author>Xueyi Fu</author><author>Duanyang Sheng</author><author>Yinghe Huang</author><author>Yuanxin Huang</author><author>Xianglong Lv</author><author>Wei Lu</author>
        <description><![CDATA[ObjectiveThis study aims to leverage publicly available databases to systematically investigate the pathogenic mechanisms of osteoarthritis (OA), with particular focus on the role of myeloid cell differentiation (MCD)-related genes. Comprehensive multidimensional analyses were performed to elucidate the potential mechanisms through which these genes contribute to the pathophysiological processes of OA. The findings of this study are expected to provide a theoretical foundation for targeting MCD-related abnormalities in OA.MethodsWe systematically integrated data acquisition, differential expression analysis, intersection with MCD-associated gene sets, machine learning-based feature selection, and multidimensional bioinformatics analysis—including functional enrichment, immune infiltration profiling (using the CIBERSORT algorithm), and structure-guided molecular docking to elucidate the molecular links between MCD and OA pathogenesis. We subsequently performed a comprehensive suite of functionally complementary downstream analyses, including nomogram construction for clinical risk prediction, receiver operating characteristic (ROC) curve analysis to assess diagnostic performance, decision curve analysis (DCA) to evaluate clinical utility, and structure-based molecular docking to probe potential ligand–target interactions.ResultsWe identified eight key genes (GRP183, MFAP2, NDP, TF, TFRC, TYROBP, VEGFA, and ZBTB16) through systematic screening. Following expression level validation, VEGFA, ZBTB16, and TYROBP were found to exhibit consistent expression trends and statistically significant differences across two independent datasets. Using the immunological atlas (the CIBERSORT algorithm), we estimated the infiltration levels of 22 immune cell subtypes and found that immune cell infiltration was significantly associated with OA progression and molecular subtype. Furthermore, molecular docking simulations were performed between the three key genes and two candidate therapeutic compounds, which provided preliminary insights and potential clues for future clinical drug development targeting these molecular interactions. To investigate the mRNA expression levels of the key genes, we performed real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis.ConclusionWe integrated transcriptomic data with bioinformatics approaches and machine-learning techniques in this study to identify potential biomarkers associated with OA. We identified VEGFA, ZBTB16, and TYROBP as three key MCD-associated genes in OA; we further explored their biological functions and underlying regulatory mechanisms, which were supported by the experimental validation of the expression of the key genes.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1843679</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1843679</link>
        <title><![CDATA[Network-based analysis identifies shared mechanisms between ischemic stroke and myocardial infarction and therapeutic ingredients of Buyang Huanwu Decoction]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shuaixin Zhang</author><author>Yue Lin</author><author>Wenke Xiao</author><author>Wei Chen</author><author>Sanyin Zhang</author>
        <description><![CDATA[IntroductionIschemic stroke (IS) and myocardial infarction (MI) share overlapping etiology and pathology, highlighting the need for dual-effective therapies. Buyang Huanwu Decoction (BHD) has shown efficacy against both diseases individually, but its core ingredients and dual therapeutic mechanisms remain unclear.MethodsUsing network-based analysis, we identified shared targets and convergent pathways for IS and MI, prioritized BHD ingredients by network proximity, and performed functional enrichment analyses. Molecular docking validated interactions between the key target GSK-3β and core ingredients, and surface plasmon resonance (SPR) experimentally determined the binding affinity of the top-ranked ingredient, Myricanone, to GSK-3β.ResultsThirteen shared core targets were identified, with the “Lipid and atherosclerosis” pathway as the principal common mechanism. Ten key BHD ingredients with predicted dual therapeutic effects were prioritized. Among the shared targets, GSK3B emerged as a central node. Molecular docking showed stable binding of all core ingredients to GSK-3β, with Myricanone exhibiting the strongest affinity (−9 kcal/mol), which was confirmed by SPR (KD = 55.1 µM).ConclusionThese findings elucidate the shared mechanisms of BHD against IS and MI, supporting its potential as a multi-target phytotherapeutic strategy for ischemic cardio-cerebrovascular disorders.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1841532</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1841532</link>
        <title><![CDATA[Codon usage bias and molecular evolution of Dnajc15 across avian lineages]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zeshuo Zhou</author><author>Jianke Yang</author><author>Duminda S. B. Dissanayake</author><author>Lisa Schwanz</author><author>Arthur Georges</author><author>Lei Xiong</author>
        <description><![CDATA[BackgroundDnajc15, encoding the methylation-controlled J-protein (MCJ) of the HSP40 family, is an endogenous repressor of the mitochondrial respiratory chain and a key regulator of mitochondrial metabolism. The codon usage patterns and molecular evolution of Dnajc15 in birds remain largely uncharacterized.MethodsWe present the first systematic characterization of Dnajc15 codon usage patterns and adaptive evolution across 27 bird species representative of 11 avian orders, using codon usage bias analysis, PAML-based selection pressure analysis, and gene family analysis.ResultsCodon usage bias analysis revealed a preference for A/U-ending codons and weak overall codon bias (effective codon number: 51.72–61.00). Selection analyses indicated predominant purifying selection (ω = 0.33). The M7 vs. M8 site model identified four candidate positively selected sites (positions 8, 17, 19, and 57), located in the N-terminal region and J-domain of the MCJ protein, though this signal was not corroborated by the M1a vs. M2a comparison, suggesting at most limited or episodic positive selection. Dnajc15 was retained across all examined vertebrate lineages despite widespread contraction of other Dnajc family members.ConclusionDnajc15 is a highly conserved component of the avian mitochondrial regulatory system, with limited evidence for adaptive variation at a small number of candidate sites, consistent with strong long-term functional constraint.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1822168</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1822168</link>
        <title><![CDATA[scCCVGBen for benchmarking of single-cell representation learning anchored on a centroid-coupled variational graph attention autoencoder across scRNA-seq and scATAC-seq]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zeyu Fu</author><author>Jiawei Fu</author><author>Chunlin Chen</author><author>Keyang Zhang</author><author>Junping Wang</author><author>Tianfei Ran</author><author>Song Wang</author>
        <description><![CDATA[Single-cell omics routinely profile millions of cells across the transcriptome and the epigenome. However, embeddings used for clustering, trajectory inference, and visualization remain unstable: stochastic variational autoencoders inject sampling noise at inference, and methods reported on idiosyncratic cohorts defeat head-to-head comparison. We introduce scCCVGBen, a benchmark of single-cell representation-learning methods. Its reference configuration is a centroid-coupled variational graph autoencoder built from three design choices: the centroid (deterministic posterior mean) used as the inference embedding, a coupling-regularized dual-reconstruction bottleneck, and a graph attention encoder over a k-nearest-neighbor cell–cell graph. We assess this configuration within a decoupled benchmark that varies the algorithmic core, encoder backbone, graph construction, dataset cohort, and evaluation suite as independent axes. The cohort, drawn from the Gene Expression Omnibus (GEO) and the European Nucleotide Archive (ENA), balances scRNA-seq and scATAC-seq equally and spans hematopoiesis, neuronal differentiation, immune populations, organ atlases, tumor microenvironments, and developmental time courses. Across the cohort, scCCVGBen improves average silhouette width by +0.288 and intrinsic-overall geometry by +0.233 over a stochastic variational encoder (VAE) on paired scRNA-seq; gains over scVI reach +0.341 and +0.331, and on scATAC-seq, the gain over PeakVI on intrinsic geometry reaches +0.346. Robustness analyses across 14 graph encoders and 5 graph-construction strategies show where alternative architectures remain competitive. Three paired hematopoietic case studies: sleep-disrupted bone marrow alongside a gastric tumor atlas, cord blood megakaryopoiesis alongside aged hematopoietic stem cells, and radiation-injury hematopoiesis alongside the COVID-19 bronchoalveolar landscape, recover coherent latent–gene programs spanning hematopoietic, epithelial–stromal, megakaryocytic, and antiviral-macrophage axes. The benchmark cohort, per-method scores, and per-dataset metadata are released through three companion sites: a Hugo atlas, a Next.js interactive cohort browser, and a cross-tool discovery surface, so the cohort can be inspected without cloning the source repository. The result is a stable, interpretable embedding that carries cleanly from benchmarking to biological discovery.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1812729</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1812729</link>
        <title><![CDATA[Knowledge mapping of alström syndrome research: a bibliometric and visualization analysis based on WoS data from 2000 to 2025]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Heng Zhang</author><author>Hang Fu</author><author>Xiaohui Sui</author><author>Shangan Si</author><author>Yuan Zhang</author><author>Kaifeng Li</author><author>Mengran Wang</author><author>Zhe Song</author><author>Yuxin Yang</author><author>Ziqi Liu</author><author>Guiju Zhang</author>
        <description><![CDATA[Alström syndrome (ALMS) is an ultra-rare autosomal recessive disorder caused by mutations in the ALMS1 gene, leading to a complex spectrum of multi-organ failure, including early-onset sensory loss, obesity, and cardiomyopathy. Despite its clinical significance, a systematic overview of the global research landscape and the intellectual evolution of this field over the past 2 decades remains absent. This study was undertaken to conduct a comprehensive bibliometric and visualization analysis of ALMS research from 2000 to 2025, aiming to identify foundational contributions, evaluate international collaboration patterns, and pinpoint emerging research frontiers. Utilizing data from the Web of Science Core Collection, 345 relevant English-language articles and reviews were analyzed using VOSviewer, CiteSpace, and the R-bibliometrix package. Our findings revealed an exponential growth phase in publications post-2020, with developed nations dominating the research output and Jackson Laboratory serving as a critical international hub. Prolific contributors such as Jan D. Marshall and Pietro Maffei have established dense collaboration networks that facilitate the transition of ALMS research from early genetic characterization to contemporary precision medicine. Keyword and co-citation analysis further highlight a thematic shift toward “ciliopathy” contexts, with recent hotspots focusing on “whole exome sequencing” and the management of “cardiomyopathy”. These results imply that while our understanding of ALMS pathogenesis is maturing, significant challenges in phenotypic heterogeneity and the lack of targeted therapies persist. Future research should prioritize interdisciplinary resource integration and the application of advanced genomics to optimize clinical management and patient outcomes in this complex rare disease.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fgene.2026.1851372</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fgene.2026.1851372</link>
        <title><![CDATA[Editorial: Refining precision medicine through AI and multi-omics integration]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Viola Bianca Serio</author><author>Pu-Feng Du</author><author>Elisa Frullanti</author><author>Maria Palmieri</author>
        <description></description>
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