Bioinformatics (Oxford, England)最新文献

{"title":"massMatchR: a Shiny application for glycomics mass spectrometry data analysis.","authors":"Gábor Beke, L'uboš Kl'učár, Zuzana Pakanová, Rebeka Kodríkova, Peter Baráth, Marek Nemčovič","doi":"10.1093/bioinformatics/btag700","DOIUrl":"10.1093/bioinformatics/btag700","url":null,"abstract":"<p><strong>Motivation: </strong>The identification and analysis of glycans using MALDI-TOF mass spectrometry is a critical task in glycomics research, yet it often requires complex data interpretation and manual processing. Existing software tools frequently lack automated solutions for efficient glycan annotation, data structuring and grouping, and semi-quantitative evaluation, making large-scale glycan analysis challenging. To address these limitations, we developed massMatchR, an open-source software tool designed to streamline the identification and quantification of glycans in MALDI-TOF spectra.</p><p><strong>Results: </strong>massMatchR automates glycan identification by mapping experimentally detected m/z values from preprocessed MALDI-TOF-MS datasets to glycans from a user-defined database. The software provides both visual and tabular outputs, enabling rapid and accurate glycan interpretation. Additionally, massMatchR facilitates the export of structured data tables (e.g. Microsoft Excel) with predefined fields for m/z and intensity, supporting further analysis. A key feature of the software is its ability to perform semi-quantitative evaluation based on relative intensity calculations, allowing for comparisons across multiple samples. Freely available at http://www.imb.savba.sk/soft/massMatchR/ and on GitHub implemented in R, massMatchR offers a fast, reproducible, and customizable computational framework for glycomics and MALDI-TOF-based glycan analysis.</p><p><strong>Availability and implementation: </strong>An implementation code is available on Github at https://github.com/bekegbor/massMatchR and on Zenodo at https://doi.org/10.5281/zenodo.20697072.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SimBinder-IF: structure-aware antibody affinity optimization via efficient preference learning.","authors":"Xinyan Zhao, Yi-Ching Tang, Rivaaj Monsia, Victor J Cantu, Ashwin Kumar Ramesh, Siyu Yang, Xiaozhong Liu, Zhiqiang An, Xiaoqian Jiang, Yejin Kim","doi":"10.1093/bioinformatics/btag548","DOIUrl":"10.1093/bioinformatics/btag548","url":null,"abstract":"<p><strong>Motivation: </strong>Antibody therapeutic efficacy depends on high-affinity target engagement, yet laboratory affinity maturation is slow and costly. Most protein language models (PLMs) lack explicit training for high affinity, and current preference optimization methods introduce computational overhead without clear affinity improvements. Therefore, structure-aware and parameter-efficient approaches for antibody affinity optimization are urgently needed.</p><p><strong>Results: </strong>We propose SimBinder-IF, a structure-aware antibody optimization model trained by freezing the Evolutionary Scale Modeling inverse folding (ESM-IF) structure encoder and fine-tuning only its decoder via Simple Preference Optimization (SimPO) to prefer stronger binders. In generalization tests across seven held-out complexes (93 477 mutants), SimBinder-IF shows a 22% numerical increase in average Spearman correlation (0.26 to 0.32) compared with vanilla ESM-IF and outperforms ESM-IF on five of seven assays; however, the assay-level paired Wilcoxon test does not reach statistical significance (p=0.188). For seed-averaged top-ranking enrichment, SimBinder-IF has the highest 20-fold improvement@20 mean (0.453), whereas ESM-IF has the highest 10-fold improvement@10 mean (0.520 vs. 0.487 for SimBinder-IF). In a case study redesigning antibody F045-092 to target the A/California/04/2009 pandemic H1N1 (pdmH1N1) strain-a target the original antibody fails to bind-SimBinder-IF generates variants with markedly lower predicted binding free energy than ESM-IF (mean ΔΔG: -68.56 vs. -49.77 kcal/mol). Notably, SimBinder-IF updates only 18% of the ESM-IF parameters. Regarding generic protein-protein affinity modelling, SimBinder-IF shows class-dependent preservation with trade-offs, improving performance on antibody-antigen (AB/AG) and protease-protein inhibitor (Pr/PI) complexes while yielding slightly lower aggregate mean correlations than ESM-IF and ProteinDPO.</p><p><strong>Availability and implementation: </strong>The source code and model weights for SimBinder-IF are available at https://github.com/MSBMI-SAFE/SimBinder-IF.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13689999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sortscore: Sort-seq MAVE scoring and visualization using Python.","authors":"Caitlyn Chitwood, Emily Orr, Dustin Baldridge","doi":"10.1093/bioinformatics/btag664","DOIUrl":"10.1093/bioinformatics/btag664","url":null,"abstract":"<p><strong>Summary: </strong>A growing number of tools enable the analysis of large variant libraries produced by multiplexed assays of variant effects (MAVEs). Experiments using fluorescent reporters and fluorescence-activated cell sorting sequencing (FACS-seq or Sort-seq) can coarsely quantify a given variant's impact on phenotypes such as transcription activity. Existing bioinformatics tools for Sort-seq data broadly fall into two categories: methods that model a genotype-phenotype landscape to infer latent variant phenotypes, and methods that directly estimate individual variant scores from experimental binned counts. Within this second category, sortscore provides an activity score computed directly from observed counts without fitting a model, retaining the original experimental scale when bin median values are known. We present sortscore, a python package that incorporates a standard Sort-seq scoring method and normalization across sorted samples, technical replicates from separate sort times, and across oligos in tiled experiments. It also provides convenient heatmap visualizations. This software was used to analyze DMS experiments for the transcription factor GLI2. This work seeks to lower the barrier to entry and provide a clear starting point for scoring Sort-seq cell-based functional assays. This extends the availability of well-documented and reproducible data analysis protocols to a wider community employing MAVE techniques.</p><p><strong>Availability and implementation: </strong>The package can be downloaded from PyPI using the pip installer. The code is also freely accessible and available for reuse through a Public GitHub repository (MIT License). Installation instructions, documentation, and tutorials are accessible at the sortscore GitHub repository: https://github.com/dbaldridge-lab/sortscore. A snapshot of the code and data is available on Zenodo: https://doi.org/10.5281/zenodo.22119031.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13682839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving lipid identification and quantification: chromatogram deconvolution for LC-MS/MS workflows.","authors":"Felix Niedermaier, Denise Wolrab-Frühauf, Robert Ahrends, Dominik Kopczynski","doi":"10.1093/bioinformatics/btag695","DOIUrl":"10.1093/bioinformatics/btag695","url":null,"abstract":"<p><strong>Motivation: </strong>Lipidomics relies on mass spectrometry-based workflows to identify and quantify complex lipid species. Due to the modular architecture of lipids, including headgroups, backbones, and fatty acyl chains, distinct precursor ions often produce isobaric or identical fragment ions. This problem is amplified in data-independent acquisition (DIA), where wide isolation windows (e.g. 25 Da) allow co-eluting precursors with different m/z values to generate highly chimeric MS/MS spectra. Consequently, fragments originating from multiple precursors, including isobars, isomers, and lipid-class-specific ions, are merged into a single MS/MS spectrum. Current lipid identification strategies often process such chimeric spectra in an uncontrolled manner, assigning them to one or more candidate lipids, thereby increasing false-positive identifications.</p><p><strong>Results: </strong>Here, we introduce an algorithm that deconvolutes chimeric MS/MS spectra and chromatograms by exploiting their temporal correlation with associated precursor chromatographic profiles, independent of elution peak shape. Using simulated and real experimental lipidomics data, we demonstrate that this approach substantially improves lipid fragment assignment, reduces false-positive identifications, and enables more reliable fragment-level quantification, leading to more robust downstream statistical analyses and biological interpretations.</p><p><strong>Availability and implementation: </strong>Source code of the software library: GitLab (Apache 2.0 License): https://gitlab.com/computational-multiomics/projects/mixture-model-deconvolution; Data: Zenodo (Apache 2.0 License): https://doi.org/10.5281/zenodo.21218594.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HD-AIP: a heterogeneous dual-stream alignment-free framework for anti-inflammatory peptide prediction based on language models and CT-Net.","authors":"Jiangli Li, Quan Zou, Yansu Wang, Yifeng Bai, Hao Zhou, Mengting Niu","doi":"10.1093/bioinformatics/btag697","DOIUrl":"10.1093/bioinformatics/btag697","url":null,"abstract":"<p><strong>Motivation: </strong>Anti-inflammatory peptides (AIPs) show therapeutic potential for treating chronic and autoimmune diseases. Computational screening of these peptides remains challenging because their typically short sequences limit traditional feature extraction effectiveness, while homology-based methods incur high computational costs.</p><p><strong>Results: </strong>This study proposes HD-AIP, an alignment-free heterogeneous dual-stream prediction architecture. This framework extracts peptide features in parallel from both macroscopic and microscopic perspectives. Macroscopically, HD-AIP integrates global semantic features extracted by two large protein language models, ProtT5 and ESM-2 3B, with sequence-level physicochemical properties, followed by feature selection and LightGBM classification. Microscopically, an asymmetric parallel network named CT-Net utilizes BioVec embeddings and residue-level physicochemical features, using a CNN branch to capture local motifs and a Transformer branch to model long-range dependencies. The two streams are adaptively fused via a dynamic soft ensemble strategy. On an independent test set, HD-AIP outperforms baseline models across multiple metrics including accuracy, area under the receiver operating characteristic curve, and the Matthews correlation coefficient. These results indicate that HD-AIP improves AIP prediction performance without sequence alignment. This architecture serves as an effective computational tool for the high-throughput virtual screening and candidate discovery of AIPs.</p><p><strong>Availability and implementation: </strong>You can find the source code and dataset needed on GitHub (https://github.com/Zerofly0/HD-AIP). The required package files have been listed in the readme.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149013428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide-scale prediction of compound-protein interactions using foundation and language models based on three-dimensional structures of compounds and proteins.","authors":"Yuga Moriyama, Yuki Matsukiyo, Tsuyoshi Kimura, Hiyori Yamamoto, Yuko Sakajiri, Tomokazu Shibata, Ryusuke Sawada, Yoshihiro Yamanishi","doi":"10.1093/bioinformatics/btag710","DOIUrl":"10.1093/bioinformatics/btag710","url":null,"abstract":"<p><strong>Motivation: </strong>The identification of compound-protein interactions (CPIs) is crucial in the early stages of drug discovery. However, machine-learning (ML)-based methods based on one- and two-dimensional representations cannot capture important geometric information on the binding sites of CPIs, which limits their predictive accuracy.</p><p><strong>Results: </strong>We propose DESTIN (dual structure-guided estimation of substance-target interaction networks), an ML framework for CPI prediction on a genome-wide scale from the three-dimensional (3D) structures of compounds and proteins. We constructed compound feature vectors that spatially reflect stable 3D conformations from a quantum-chemical perspective by fine-tuning a 3D structure-based foundation model. Furthermore, we constructed protein feature vectors leveraging sequence-based structure representations and protein language models, even for proteins that do not have 3D structures determined experimentally. CPI prediction was performed using an integrative ML model on 3D structure-aware feature vectors for compounds and proteins. The effectiveness of the proposed model was validated via rigorous benchmark tests on practical scenarios. DESTIN exhibited good predictive performance, even for structurally complex and pharmacologically important membrane proteins. These results show that DESTIN is promising for various pharmaceutical applications.</p><p><strong>Availability and implementation: </strong>All source code and data supporting this study are publicly available on GitHub and archived on Zenodo (https://doi.org/10.5281/zenodo.21759701).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CSGDA: a cell state-guided graph domain adaptation network for single-cell drug response prediction.","authors":"Fen Yan, Donggui Xie, Xiyue Cao, Feiqiao Mao, Zhuhong You, Yitao Chen, Zhihua Du, Yu-An Huang","doi":"10.1093/bioinformatics/btag726","DOIUrl":"10.1093/bioinformatics/btag726","url":null,"abstract":"<p><strong>Motivation: </strong>Intratumoral heterogeneity drives cancer recurrence and metastasis, yet single-cell drug response prediction faces severe \"cross-domain\" challenges, such as applying in vitro models to in vivo tissues or inferring metastatic resistance from primary tumors. These scenarios trigger distribution shifts arising from heterogeneous sequencing platforms, distinct tissue microenvironments, and metastatic evolution-problems rarely addressed by existing methods.</p><p><strong>Results: </strong>We introduce CSGDA, a cell state-guided graph domain adaptation framework designed to predict drug responses across these biological heterogeneities. CSGDA incorporates biological priors to map gene expression into functional cell states, guiding a structure-learning module to construct robust cell topology. To mitigate distribution shifts, the model employs graph domain adaptation combined with a novel overlap penalty mechanism. Extensive benchmarks on five scRNA-seq datasets demonstrate that CSGDA outperforms the state-of-the-art method SSDA4Drug by approximately 6 percentage points in both ACC and AUPR. Beyond prediction accuracy, we employed integrated gradients to effectively pinpoint key genes involved in drug resistance within a challenging cross-metastasis cisplatin dataset. These findings underscore CSGDA's superior performance in single-cell drug response prediction and its potential in resolving single-cell heterogeneity, paving the way for precision medicine.</p><p><strong>Availability and implementation: </strong>Source code is available at https://github.com/yanfen-git/CSGDA-code and archived at https://doi.org/10.5281/zenodo.21759274. Data are available at https://doi.org/10.5281/zenodo.21770737.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13680098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SNOOPPI: a sequence-normalized database of on- and off-target protein-protein interactions.","authors":"Sophia Vincoff, Pranam Chatterjee","doi":"10.1093/bioinformatics/btag736","DOIUrl":"10.1093/bioinformatics/btag736","url":null,"abstract":"<p><strong>Motivation: </strong>Protein-protein interactions (PPIs) are central to biological processes and therapeutic interventions. While many databases document known PPIs, these resources often lack evidence-based negatives and clear identification of the specific proteoforms tested.</p><p><strong>Results: </strong>Here, we introduce SNOOPPI, a Sequence-Normalized database of on- and off-target protein-protein interactions, which represents the first unified dataset of binary PPIs that is isoform, post-translational modification, mutation, and binding site aware. By defining a PPI as a direct, physical interaction between two amino acid sequences, SNOOPPI overcomes several persistent limitations of existing PPI databases. SNOOPPI was curated from the IntAct database, taking full advantage of its experimental metadata and feature annotations to reclassify and uncover new PPIs. The final dataset comprises over 33.6K positive interactions and 5.3K negative interactions. SNOOPPI also retains over 835K unresolved interactions, explicitly capturing gaps in the experimental literature. Beyond its usefulness as a reference dataset for the scientific community, SNOOPPI has the potential to serve as a high-confidence foundation for sequence-based modeling, benchmarking, and generative design of novel protein and peptide interactors.</p><p><strong>Availability and implementation: </strong>The full database can be accessed and downloaded through an interactive Hugging Face page: https://huggingface.co/datasets/ChatterjeeLab/SNOOPPI. All code is open-source and maintained at https://github.com/sophievincoff/snooppi. The database and associated code will be updated periodically to incorporate new IntAct releases. The specific version of the code associated with this publication is archived at the following Zenodo repository: https://doi.org/10.5281/zenodo.22116350.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VAEBAC: a representation-learning framework for proteome-scale prediction of amyloidogenic proteins and functional stratification of nucleic-acid-binding proteins.","authors":"Muhammad Uzair Ashraf, Owais Ahmad, Rizwan Hasan Khan","doi":"10.1093/bioinformatics/btag745","DOIUrl":"10.1093/bioinformatics/btag745","url":null,"abstract":"<p><strong>Motivation: </strong>Nucleic-acid-binding proteins (NABPs) govern transcription, replication and genome organization, yet the distribution of aggregation susceptibility within regulatory proteomes remains unresolved. Here, we systematically examine amyloidogenic potential across the nucleic-acid-binding proteomes of four phylogenetically diverse organisms-Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, and Homo sapiens-spanning prokaryotes and eukaryotes, to determine whether aggregation-prone sequence architectures are selectively constrained within transcriptional networks.</p><p><strong>Results: </strong>Using VAEBAC, a representation-learning framework that integrates large-scale amyloid-family sequences with experimentally validated annotations, we performed proteome-scale prediction of amyloidogenicity across nucleic-acid-binding proteins in each organism. Predicted amyloidogenic proteins were consistently and significantly enriched in Gene Ontology categories associated with regulation of DNA-templated transcription, RNA biosynthesis, and gene expression across all four organisms, a pattern validated using three independent annotation tools. Fisher's exact test confirmed significant enrichment of amyloidogenic NABPs among transcription regulators in eukaryotic proteomes. Residue-level mapping demonstrated spatial separation between predicted aggregation-prone regions and DNA-binding interfaces, suggesting structural compatibility between regulatory function and conditional aggregation. Experimental validation confirmed fibrillar assembly of selected proteins. These findings support a conserved model in which aggregation susceptibility is functionally stratified across transcriptional regulatory tiers rather than uniformly suppressed within NABPs, a principle conserved across 3.5 billion years of evolution.</p><p><strong>Availability and implementation: </strong>VAEBAC is freely accessible at https://www.vaebac.com.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13682611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Control-guided refinement of partially specified Boolean networks: applications to RTK signaling.","authors":"","doi":"10.1093/bioinformatics/btag773","DOIUrl":"10.1093/bioinformatics/btag773","url":null,"abstract":"","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":"42 10","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13680630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}