Journal of Computational Biology最新文献

{"title":"Exact and Asymptotic Counts of Binary Phylogenetic Networks with a Few Reticulation Events.","authors":"Hao Yu, Michael Fuchs, Guan-Ru Yu, Louxin Zhang","doi":"10.1177/15578666251400786","DOIUrl":"10.1177/15578666251400786","url":null,"abstract":"<p><p>Phylogenetic networks are powerful models for representing evolutionary history in studies of genome evolution involving reticulation events. The complex structure of phylogenetic networks makes their inference challenging, especially for large numbers of taxa. To better understand the full space of phylogenetic networks, we derive closed-form formulas for counting binary phylogenetic networks with two and three reticulation events. In addition, we also use our approach to give a simple proof of the known asymptotic result for a fixed number of reticulations.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"386-400"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146197671","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 REsampling and Visual EvALuation Method to Detect and Map Local Model Violations During Biomolecular Sequence Analysis.","authors":"Meijun Gao, Kevin J Liu","doi":"10.1177/15578666261424921","DOIUrl":"10.1177/15578666261424921","url":null,"abstract":"<p><p>A fundamental assumption in phylogenetics and phylogenomics is that a single, global evolutionary model can adequately characterize the substitution processes operating across all sites in a molecular sequence alignment. However, this assumption is frequently violated in practice due to heterogeneity in evolutionary processes, leading to local model mis-specification and potential bias in downstream inference. While a variety of statistical and machine learning-based approaches have been developed to address this issue, these methods often rely on restrictive model assumptions or are designed for narrowly scoped applications, limiting their generalizability across diverse datasets and evolutionary contexts. Here, we present REVEAL (\"REsampling and Visual EvALuation\"), a general-purpose statistical framework for detecting and localizing model mis-specification in biomolecular sequence data. REVEAL operates without introducing additional assumptions beyond those inherent to standard global model-based analyses. It employs sequence-aware statistical resampling to construct a local support matrix along the sequence alignment, facilitating the identification of site-level model violations. Through extensive simulation experiments, we demonstrate that REVEAL achieves robust control of both type I and type II errors, with precision of <math><mrow><mn>90</mn><mi>%</mi></mrow></math> or greater and recall of <math><mrow><mn>85</mn><mi>%</mi></mrow></math> or greater across diverse evolutionary scenarios involving different sources of model heterogeneity, varying dataset sizes in terms of sequence length and number of taxa, and other experimental factors. We further apply REVEAL to genomic data from mouse and mosquito, uncovering localized model violations that are consistent with previously reported biological signals. These results establish REVEAL as a flexible and effective tool for evaluating model adequacy in phylogenetic and phylogenomic analyses.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"482-498"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146258234","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":"Special Issue: RECOMB Satellite Conference on Comparative Genomics (RECOMB-CG 2025).","authors":"Giltae Song","doi":"10.1177/15578666261426008","DOIUrl":"10.1177/15578666261426008","url":null,"abstract":"","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"385"},"PeriodicalIF":1.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146227076","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 Algorithms for Computing Jaro Similarity.","authors":"Joyanta Basak, Nidhibahen Shah, Kenneth Haase, Anup Mathur, Krista Park, Rebecca Steorts, Daniel Weinberg, Sartaj Sahni, Sanguthevar Rajasekaran","doi":"10.1177/15578666261421965","DOIUrl":"https://doi.org/10.1177/15578666261421965","url":null,"abstract":"<p><p>The Jaro and Jaro-Winkler similarity measures are fundamental tools for character-based string comparison, with widespread use in applications such as record linkage, entity resolution, and natural language processing. Although their accuracy in capturing typographical and transpositional errors has made them popular, traditional implementations suffer from high computational cost, especially when applied to large datasets. Previously, we proposed a Jaro similarity algorithm that reduces the time complexity from quadratic to linear. The proposed linear time algorithm can compute the Jaro similarity between two strings significantly faster if the strings are sufficiently long. In this article, we introduce enhanced algorithms for computing both Jaro and Jaro-Winkler similarity that improve the runtime, including in handling shorter strings. Furthermore, we propose some techniques to drastically reduce the computing time for the case where a set of strings is repeatedly compared among themselves, making the algorithms particularly well-suited for large-scale record linkage tasks.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261421965"},"PeriodicalIF":1.6,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147390128","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":"HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessment.","authors":"Inass Soukarieh, Gerhard Hessler, Hervé Minoux, Marcel Mohr, Friedemann Schmidt, Jan Wenzel, Pierre Barbillon, Hugo Gangloff, Pierre Gloaguen","doi":"10.1177/15578666251410587","DOIUrl":"10.1177/15578666251410587","url":null,"abstract":"<p><p>Mathematical modeling in systems toxicology enables a comprehensive understanding of the effects of pharmaceutical substances on cardiac health. However, the complexity of these models limits their widespread application in early drug discovery. In this article, we introduce a novel approach to solving parameterized models of cardiac action potentials (CAPs) by combining meta-learning techniques with systems biology-informed neural networks (SBINNs). The proposed method, hyperSBINN, effectively addresses the challenge of predicting the effects of various compounds at different concentrations on CAPs, outperforming traditional differential equation solvers in speed. Our model efficiently handles scenarios with limited data and complex parameterized differential equations. The hyperSBINN model demonstrates robust performance in predicting APD90 values, indicating its potential as a reliable tool for modeling cardiac electrophysiology and aiding in preclinical drug development. This framework represents an advancement in computational modeling, offering a scalable and efficient solution for simulating and understanding complex biological systems.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"281-300"},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085904","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":"NeuroMDAVIS: Visualization of Single-Cell Multi-Omics Data under Deep Learning Framework.","authors":"Chayan Maitra, Dibyendu B Seal, Vivek Das, Rajat K De","doi":"10.1177/15578666261423965","DOIUrl":"10.1177/15578666261423965","url":null,"abstract":"<p><p>Single-cell technologies have favored extensive advancements in cell-type discovery, cell state identification, development of lineage tracing, and disease understanding among others. Further, single-cell multi-omics data generated using modern technologies provide several views of omics contribution for the same set of cells. Analyzing these views of multi-omics data is hindered by large dimensions of the same. In this regard, one effective approach is dimensionality reduction and thereby visualization (in 2D or 3D space) of the integrated views of multi-omics data. However, dimension reduction and visualization of these datasets remain a challenging task since obtaining a low-dimensional embedding that preserves information about local and global structures in data is difficult. Moreover, combining different views obtained from each omics layer to interpret the underlying biology is even more challenging. In this work, we introduce NeuroMDAVIS, a novel unsupervised deep neural network model, for joint visualization of biological datasets having multiple modalities. Joint visualization refers to transforming the feature space of each modality and combining them to produce a latent embedding that supports visualization of the multi-modal dataset in the newly transformed feature space. NeuroMDAVIS transforms the feature space of each modality and integrates them into a shared latent space, capturing both modality-specific and common information across different layers. The model effectively learns both local and global relationships within the data, providing a meaningful low-dimensional representation for further analysis. NeuroMDAVIS is able to capture both individual modality-specific information as well as common information across all modalities. When it comes to visualization capability, NeuroMDAVIS competes against state-of-the-art visualization models such as t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Fast interpolation-based t-SNE (Fit-SNE), the Siamese network-based visualization method (IVIS), and the manifold learning-based generalized version of UMAP, called MultiMAP. Downstream analyses have reflected effective classification and clustering performance over all the datasets, in terms of accuracy, precision, recall, F1 score, and various cluster validity indices. To the best of our knowledge, NeuroMDAVIS is the first model to offer joint visualization for multi-modal biological datasets. It competes with the state-of-the-art visualization methods, providing a robust and efficient approach for understanding complex multi-omics data.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"301-317"},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290222","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":"PMScanR: An R Package for the Large-Scale Identification, Analysis, and Visualization of Protein Motifs.","authors":"Jan P Jastrzebski, Monika Gawronska, Wiktor Babis, Miriana Quaranta, Damian Czopek","doi":"10.1177/15578666261423966","DOIUrl":"10.1177/15578666261423966","url":null,"abstract":"<p><p>Proteins play a crucial role in biological processes, with their functions closely related to structure. Protein functions are often associated with specific motifs, which are short amino acid sequences exhibiting particular patterns. Most bioinformatics tools focus on identifying known motifs and they lack the ability to analyze the impact of single substitutions on entire domains or motifs. To address these limitations, we developed PMScanR (Protein Motif Scanner in R), an R package that automates the prediction and evaluation of the impact of single amino acid substitutions on protein motif occurrence in large datasets. In addition, existing tools do not support comparative analysis of multiple motifs across multiple sequences-a key feature that PMScanR was designed to provide. The package integrates various methods to facilitate motif identification, characterization, and visualization. It includes functions for running PS-Scan, a PROSITE database tool. Additionally, PMScanR supports format conversion to GFF, enhancing downstream analyses such as graphical representation and database integration. The library offers multiple visualization tools, including occurrence plots, sequence logos, and pie charts, enabling a deeper understanding of motif distribution and conservation. Through its integration with PROSITE, PMScanR provides access to up-to-date motif data, making it a valuable tool for biological and biomedical research, particularly in protein function annotation and therapeutic target identification. PMScanR is freely available under the GPL license and is distributed through Bioconductor (https://bioconductor.org/packages/PMScanR) and GitHub (https://github.com/prodakt/PMScanR).</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"379-384"},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147326209","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":"Stability Analysis of Age-Structured Stochastic Delayed Population-Toxicant Model with Lévy Noise.","authors":"Yanyan Du, Zong Wang","doi":"10.1177/15578666261423556","DOIUrl":"10.1177/15578666261423556","url":null,"abstract":"<p><p>In this article, we develop a new age-structured stochastic delayed toxicant-population model with Lévy noise. Some novel criteria for the existence of a global positive solution and the mean square <i>h</i>-<i>stability</i> of the addressed model are given. The key technique used to prove the results is the Lyapunov functional approach and Itô's formula. Finally, two numerical examples are provided to illustrate the effectiveness of our criteria.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"355-378"},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443975","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":"Gene Network Enrichment Analysis and Its Application to Explore Enriched Immune Disease Pathways for Gene Network of Acute Myeloid Leukemia Cell Lines.","authors":"Heewon Park, Satoru Miyano","doi":"10.1177/15578666261424277","DOIUrl":"10.1177/15578666261424277","url":null,"abstract":"<p><p>Recent advances in gene network analysis have improved our understanding of complex disease mechanisms; however, interpreting estimated gene networks remains challenging. Existing methods for pathway enrichment analysis focus on gene sets and therefore fail to capture interaction-level information that is critical for understanding disease-related molecular interplays. Here, we propose a novel computational strategy for gene network enrichment analysis (GNEA) that evaluates pathway overrepresentation at the edge level, explicitly incorporating both network structure and the biological importance of hub genes. Thus, our strategy provides reliable biological results. We demonstrated the efficacy of our approach through Monte Carlo simulations of myeloid neoplasms and pan-cancer-related pathway-enriched gene network analysis. The proposed strategy was applied to immune disease pathway-enriched gene network analysis. Our results identify <i>inflammatory bowel disease</i>-related pathways enriched in both acute myeloid leukemia (AML)-aged and AML-young networks, and <i>asthma</i>-related pathways enriched in healthy-young networks. Our results suggested that \"activation of <i>CD40</i> and <i>CD40LG</i>\" and \"mutual activation between <i>HLA-DPB1</i> and <i>IL4R</i>\" are potential markers to uncover AML-related mechanisms. Overall, this study demonstrates that GNEA provides a powerful framework for uncovering biologically meaningful interaction-level insights into complex diseases.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"336-354"},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480643","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":"DP-OTG: A Feature-Free Deep Learning Model for Accurate Prediction of Human O-Linked Threonine Glycosylation Sites.","authors":"Thi-Xuan Tran, Nguyen-Quoc-Khanh Le, Duc-Hau Le, Van-Nui Nguyen","doi":"10.1177/15578666261424278","DOIUrl":"10.1177/15578666261424278","url":null,"abstract":"<p><p>Protein O-linked threonine glycosylation (OTG) is a crucial post-translational modification in eukaryotic species, playing a vital role in diverse biological processes. In humans, dysregulation of OTG has been associated with serious diseases, including cancer and neurological disorders. However, experimental detection of OTG sites remains costly and labor-intensive, underscoring the need for effective computational approaches. In this study, we introduce DP-OTG, a feature-free deep learning model for the accurate prediction of human OTG sites. Unlike existing tools that rely heavily on handcrafted features or large-scale pretrained language models, DP-OTG employs a hybrid architecture combining multi-kernel convolutional neural networks, bidirectional long short-term memory, and a trainable embedding layer to automatically learn sequence patterns directly from raw protein sequences. This end-to-end framework captures both local and long-range sequence dependencies without the need for manual feature engineering. Extensive evaluations using 10-fold cross-validation and independent testing demonstrate that DP-OTG achieves superior predictive performance, with an accuracy of 88.8% and an Matthew's Correlation Coefficient (MCC) of 0.776 on the balanced test set, and an accuracy of 89.3% and an MCC of 0.661 on the imbalanced test set, outperforming several state-of-the-art predictors. In addition, to comprehensively assess the discriminative power and generalization ability of DP-OTG in predicting human OTG sites, we employed t-distributed stochastic neighbor embedding to visualize the feature representations before and after training. These results underscore the effectiveness of DP-OTG in extracting robust features for accurate OTG site prediction, even under challenging data distributions. Our findings highlight DP-OTG as a robust, efficient, and scalable tool for human OTG site prediction. All the code and resources related to this study have been made freely accessible at: https://github.com/nuinvtnu/DP-OTG/.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"318-335"},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147317226","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}