Journal of Computational Biology最新文献

{"title":"NCTDA: Nearest Neighbor Gaussian Process-Based Cell Type-Specific Spatially Variable Gene Detection Analysis.","authors":"Zhixin Shi, Ziyan Sun, Yuan Zhang, Guanpeng Qi, Jie Gao","doi":"10.1177/15578666261466689","DOIUrl":"10.1177/15578666261466689","url":null,"abstract":"<p><p>A primary task of spatial transcriptomics is detecting spatially variable genes (SVGs). Many genes may show spatially heterogeneous expression in specific cell types while showing spatial randomness across the whole tissue, thereby defining cell type-specific SVGs (ctSVGs). This study aims at detecting not only ctSVGs but also SVGs. Here, we construct a novel analysis framework NCTDA, which employs a nearest neighbor Gaussian process (NNGP) model to incorporate cell type composition into the spatial modeling of gene expression, is capable of linearly scaling with the number of spatial spots, unlike the cubic scalability of most methods. NCTDA performs hypothesis testing for different detection purposes, namely, obtaining SVGs by testing the variance components associated with overall spatial effects and obtaining ctSVGs by testing the coefficients associated with cell types. As a computationally scalable framework, NCTDA enables robust research of large-scale spatial transcriptomics data. Through simulation and real data applications, the results confirm the accuracy and efficiency of NCTDA, enabling it to characterize distinct cellular states and gene modules within structurally complex tissues comprising multiple cell types. It delivers more profound insights into the spatial expression characteristics of genes and cellular functional heterogeneity during tissue development processes and disease states.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"929-942"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148421488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Structure-Aware Multimodal Framework for Drug-Target Interaction Prediction via Heterogeneous Graph Learning.","authors":"Hua Qian, Deng Pan, Liangpeng Nie, Yelu Jiang, Lijun Quan","doi":"10.1177/15578666261469586","DOIUrl":"10.1177/15578666261469586","url":null,"abstract":"<p><p>Predicting drug-target interactions is critical for drug discovery, yet many deep learning methods overlook atom-residue-level relationships. We propose Protein Heterogeneous Graph learning for Drug-Target Interaction prediction (PHGDTI), a multimodal framework that integrates sequence and structural cues for binding prediction. Drug and protein sequences are embedded with Mol2Vec and Tasks Assessing Protein Embeddings (TAPE) and refined by a self-attention module. In parallel, a drug-protein graph encoder models three complementary graphs: a drug atom graph, a protein residue graph, and a heterogeneous atom-residue graph. Graph attention layers propagate intra- and intermolecular information, and SAGPooling yields compact structural representations. Fusing these structural and sequence features enables accurate affinity estimation. Experiments on the Davis kinase dataset and GalaxyDB dataset show PHGDTI surpasses competitive baselines, and ablation results highlight the benefit of heterogeneous graph modeling.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"964-976"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148535888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Link Prediction Based on Subgraph Learning in Biological Networks.","authors":"Xiaolong Liu, Jianxia Chen, Wenzhe Chen, Haoran Chen, Zhaoyang Hu, Shihua Yang, Cheng Fang, Zhongwei Huang","doi":"10.1177/15578666261469585","DOIUrl":"10.1177/15578666261469585","url":null,"abstract":"<p><p>Recently, link prediction (LP) based on graph neural networks (GNNs) methods has achieved notable successes in biological networks (BNs), since it can reveal the organizational principles, functional mechanisms, and dynamic properties of biological systems. However, these LPs still face some significant challenges that need to be addressed in BNs. The first is the complex and heterogeneous characteristics of BNs. Moreover, BN structures often have the dynamic addition and removal of nodes and edges over time or across physiological states. Afterward, there are asymmetries and hierarchical modularity in the structures of BNs. Finally, high computational complexity has resulted from the above challenges in BNs. Therefore, this article proposes a novel GNN-based LP model via local clustering and subgraphs, termed LCS in short, to address these issues in BNs. LCS introduces a subgraph-based GNN approach to effectively address the heterogeneous characteristics inherent in complex BNs, along with the consequent challenges of asymmetry and hierarchical modularity. Furthermore, LCS designs a dynamic local subgraph extraction (SE) mechanism based on heat kernel diffusion and the Chopper pruning algorithm. This mechanism leverages the effective local clustering properties of heat diffusion and uses Chopper to achieve linear-time SE, mitigating subgraph size explosion and enhancing LP efficiency. Additionally, by imposing diversity regularization constraints, the method reduces computational complexity and improves generalization performance. Experimental results on four BN benchmarks demonstrate that LCS achieves significant improvements over existing state-of-the-art LP methods. The implementation of LCS is publicly available at https://github.com/XL0104/LCS-Model.git.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"943-963"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148578871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Petri Net Analysis of the MAPK/ERK Cascade Coupled to the GTP:RAS Complex in the EGFR Signaling Network.","authors":"Janet B Jones-Oliveira, Hans-Joseph B Oliveira, Joseph S Oliveira","doi":"10.1177/15578666261469890","DOIUrl":"10.1177/15578666261469890","url":null,"abstract":"<p><p>The MAP kinase cascade of the epidermal growth factor receptor network is analyzed to further develop and demonstrate the application of Petri net models for the analysis of cellular systems. The mitogen-activated protein kinase/extracellular-regulated kinase (ERK) (subreaction H) model is then coupled to the model previously developed for the GTP:Ras complex (subreaction A) to demonstrate the computational scalability and analytical utility of the Shannon-like partition information entropy metric in the analysis of ever larger biochemical signaling networks. Unique minimal cycles (UMCs) have been shown to provide a natural partitioning of biochemical reaction networks. The complete listing of UMCs for two coupled subnetworks is found to contain all the UMCs of the individual subnetworks plus additional UMCs introduced by the cross-coupling of the two subnetworks by the shared species, in this case, ERK-PP and GTP:Ras, leading to a significant increase in the number and complexity of UMCs available for the control of the coupled subnetworks. The partition information entropy metric is used to rank order the UMCs to provide insight into the most critical pathways, thereby identifying which loops provide the most dynamic controllability over the cell's fate. Analysis of UMCs with some feed-backward transitions is found to be at least as important to the functioning of the network as UMCs with feed-forward only transitions.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"977-1015"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148712581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Morphology and Gene Expression of Neural Cells in Unpaired Single-Cell Data Using GeoAdvAE.","authors":"Jinqiu Turbo Du, Tom Chartrand, Suman Jayadev, Katherine E Prater, Kevin Z Lin","doi":"10.1177/15578666261492321","DOIUrl":"10.1177/15578666261492321","url":null,"abstract":"<p><p>Cellular morphological transitions are observed across many diseases, yet their functional role remains unclear because few technologies profile form and function in the same cell. Linking single-cell morphology to transcriptomics is difficult: The two modalities share no feature correspondence and are typically measured in different cells. We present GeoAdvAE, a geometry-aware adversarial autoencoder for diagonal (unpaired) integration of single-cell morphology and single-cell RNA sequencing. GeoAdvAE couples modality-specific variational autoencoders with a Gromov-Wasserstein regularizer and an adversarial discriminator to embed unpaired morphologies and transcriptomes into a shared latent space that preserves both reconstruction fidelity and cross-modal geometry. Using patch-seq neurons with joint morphology-RNA measurements as ground truth, GeoAdvAE attains the best cross-modal cell-type matching accuracy among diagonal integration methods, outperforming optimal-transport, latent-alignment, and adversarial baselines. Applied to 98 CAJAL-quantified microglial morphologies and 31,948 single-cell transcriptomes from the 5xFAD Alzheimer's disease model, GeoAdvAE recovers a one-dimensional axis that aligns the two modalities. Integrated-gradient attribution highlights transcriptomic shifts (DNA repair in ramified microglia; cell killing in amoeboid microglia), nominates gene markers (<i>Ms4a6b</i>; <i>Ftl1</i>/<i>Fth1</i>), and reveals disease-associated microglia signatures that are decoupled from morphology. GeoAdvAE provides a scalable, interpretable approach to connecting cellular \"form\" and \"function\" when joint profiling of morphology and transcriptomics is impractical. Our method is publicly available at https://github.com/turbodu222/GeoAdVAE.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261492321"},"PeriodicalIF":1.3,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpolating and Extrapolating Node Counts in Colored Compacted de Bruijn Graphs for Pangenome Diversity.","authors":"Luca Parmigiani, Pierre Peterlongo","doi":"10.1177/15578666261486474","DOIUrl":"https://doi.org/10.1177/15578666261486474","url":null,"abstract":"<p><p>A pangenome is a collection of taxonomically related genomes, often from the same species, serving as a representation of their genomic diversity. The study of pangenomes, or pangenomics, aims to quantify and compare this diversity, which has significant relevance in fields such as medicine and biology. Originally conceptualized as sets of genes, pangenomes are now commonly represented as pangenome graphs. These graphs consist of nodes representing genomic sequences and edges connecting consecutive sequences within a genome. Among possible pangenome graphs, a common option is the compacted de Bruijn graph (cdBG). In our work, we focus on the colored cdBG (ccdBG), where each node is associated with a set of colors that indicate the genomes traversing it. In response to the evolution of pangenome representation, we introduce a novel method for comparing pangenomes by their node counts, addressing two main challenges: the variability in node counts arising from graphs constructed with different numbers of genomes and the large influence of rare genomic sequences. We propose an approach for interpolating and extrapolating node counts in ccdBGs, adjusting for the number of genomes. To tackle the influence of rare genomic sequences, we apply Hill numbers, a well-established diversity index previously utilized in ecology and metagenomics for similar purposes, to proportionally weight both rare and common nodes according to the frequency of genomes traversing them.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261486474"},"PeriodicalIF":1.3,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Xoublet: An Extreme-Scale Doublet Detection Algorithm for Cardiac Single-Cell Transcriptomics.","authors":"Ping Xu, Ping Zhou, Fuqiang Hu, Hang Zhang, Pengfei Lu","doi":"10.1177/15578666261489802","DOIUrl":"https://doi.org/10.1177/15578666261489802","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of cardiac cell composition and its underlying pathogenesis. In scRNA-seq data analysis, doublet detection plays a pivotal role in quality control. However, these methods often face challenges related to high computational demands and extended runtimes, particularly in large-scale datasets and rare cell identification. To address these issues, this study introduces the eXtreme-scale Doublet Analyzer (Xoublet) algorithm, which projects single-cell transcriptomic data into principal component analysis space and simulates doublets. Xoublet constructs a hierarchical tree to facilitate rapid screening of similar data, expediting neighborhood searches. Xoublet was compared with Scrublet and DoubletFinder on human-mouse and Cell Hashing datasets with experimentally derived labels, while method-specific calls were examined in an annotated cardiac atlas without independent doublet ground truth. Computational scalability was evaluated separately on HeartMap. Accuracy varied across datasets: Xoublet performed similarly to Scrublet in the cell-line Cell Hashing benchmark but was less discriminative in the human-mouse and peripheral blood mononuclear cell benchmarks. On 2,479,674 HeartMap profiles, Xoublet completed scoring in 574.8 seconds with 4.14 GiB peak resident memory, compared with 1060.6 seconds and 22.81 GiB for the Scrublet core. These findings indicate that Xoublet provides computational advantages for large-scale scoring, although its detection accuracy and sensitivity to specific cell states remain data-dependent.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261489802"},"PeriodicalIF":1.3,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149039002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast and Flexible Flow Decompositions in General Graphs via Dominators.","authors":"Francisco Sena, Alexandru I Tomescu","doi":"10.1177/15578666261486472","DOIUrl":"https://doi.org/10.1177/15578666261486472","url":null,"abstract":"<p><p>Multi-assembly methods rely at their core on a flow decomposition problem, namely, decomposing a weighted graph into weighted paths or walks. However, most results over the past decade have focused on decompositions over directed acyclic graphs (DAGs). This limitation has led to either purely heuristic methods or, in applications, transforming a graph with cycles into a DAG via preprocessing heuristics. In this article, we show that flow decomposition problems can also be solved in practice on general graphs with cycles via a framework that yields fast and flexible mixed-integer linear programming (MILP) formulations. Our key technique relies on the graph-theoretical notion of a <i>dominator tree</i>, which we use to find all <i>safe sequences of edges</i> that are guaranteed to appear in some walk of any flow decomposition. We generalize previous results from DAGs to cyclic graphs by showing that maximal safe sequences correspond to extensions of common leaves of two dominator trees, and that all such sequences can be found in time linear in their size. Using these, we can accelerate MILPs for <i>any</i> flow decomposition into walks in general graphs by setting suitable variables encoding solution walks to (at least) 1 and by setting to 0 other walk variables that are nonreachable to and from safe sequences. This reduces model size and eliminates costly linearizations of MILP variable products. We experiment with three decomposition models (minimum flow decomposition, least absolute errors, and minimum path error) on four bacterial datasets. Our preprocessing enables up to 1000-fold speedups and solves many instances that would otherwise time out in under 30 seconds. We thus hope that our dominator-based MILP simplification framework, together with the accompanying software library, can serve as building blocks for multi-assembly applications.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261486472"},"PeriodicalIF":1.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Learning of Drug-Drug Combination and Drug-Drug Interaction via Coupled Tensor-Tensor Factorization with Side Information.","authors":"Xiaoge Zhang, Zhengyu Fang, Kaiyu Tang, Huiyuan Chen, Jing Li","doi":"10.1177/15578666261485497","DOIUrl":"https://doi.org/10.1177/15578666261485497","url":null,"abstract":"<p><p>Targeted drug therapies offer a promising approach for treating complex diseases, with combinational drug therapies often employed to enhance therapeutic efficacy. However, unintended drug-drug interactions (DDIs) may undermine treatment outcomes or cause adverse side effects. In this work, we propose a novel joint learning framework for the simultaneous prediction of effective drug combinations and DDIs, based on coupled tensor-tensor factorization. Specifically, we model drug combination therapies and DDI by representing drug-drug-disease associations and DDI profiles as coupled three-way tensors. To address the challenges of data incompleteness and sparsity, the proposed model integrates auxiliary drug similarity information, such as chemical structure similarities, drug-specific side effects, drug target profiles, and drug inhibition data on cancer cell lines, within a multiview learning framework. For optimization, we adopt a modified alternating direction method of multipliers (ADMM) algorithm with non-negativity constraints. In addition to standard tensor completion tasks, we further evaluate the proposed method under a more realistic <i>new-drug prediction</i> setting, where all interactions involving a previously unseen drug are withheld. This scenario closely aligns with real-world applications, in which reliable predictions for emerging or under-studied compounds are essential. We evaluate the proposed method (named SI-ADMM for Side Information-ADMM) on a comprehensive dataset compiled from multiple sources, including DrugBank, the Continuous Drug Combination Database (CDCDB), the Side Effect Resource (SIDER), and PubChem. Our experiments show that SI-ADMM maintains robust performance and achieves the best results comparing to other tensor factorization approaches, with or without auxiliary information, particularly in the new-drug prediction setting. The implementation of our method is publicly available at: https://github.com/Xiaoge-Zhang/SI-ADMM.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261485497"},"PeriodicalIF":1.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148968915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unique Molecular Identifiers Don't Need to be Unique: A Collision-Aware Estimator for RNA-Seq Quantification.","authors":"Dylan Agyemang, Rafael A Irizarry, Tavor Z Baharav","doi":"10.1177/15578666261477771","DOIUrl":"10.1177/15578666261477771","url":null,"abstract":"<p><p>RNA-sequencing (RNA-seq) relies on Unique Molecular Identifiers (UMIs) to accurately quantify gene expression after PCR amplification. Longer UMIs minimize collisions, where two distinct transcripts are assigned the same UMI, at the expense of increased sequencing and synthesis costs. However, it is not clear how long UMIs need to be in practice, especially given the nonuniformity of the empirical UMI distribution. In this work, we develop a method-of-moments estimator that accounts for UMI collisions, accurately quantifying gene expression and preserving downstream biological insights. We show that UMIs need not be unique: shorter UMIs can be used with a more sophisticated estimator.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261477771"},"PeriodicalIF":1.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148969009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}