Bioinformatics advances最新文献

{"title":"NeuraGraph: a visual workflow framework for reproducible biomedical text mining with LLM-agents.","authors":"Lei Zhao, Changning Ren, Xinning Liu, Li Han, Yingnan Fan, Ling Kang, Quan Guo","doi":"10.1093/bioadv/vbag240","DOIUrl":"10.1093/bioadv/vbag240","url":null,"abstract":"<p><strong>Summary: </strong>Biomedical text mining increasingly demands hybrid pipelines integrating traditional NLP toolkits and LLM-based agents, yet building reproducible and modular workflows remains challenging. We present NeuraGraph, a lightweight open-source Python framework for reproducible biomedical text mining workflows integrating traditional NLP components and LLM-based agents. NeuraGraph provides hierarchical workflow composition through reusable subgraphs, built-in batch experimentation with standardized evaluation, and an LLM-powered reporting and refinement engine for workflow optimization. We evaluated NeuraGraph on named entity recognition and chemical-induced disease relation extraction using the BioCreative V CDR benchmark. An optimized relation extraction workflow improved Micro-F1 from 0.656 to 0.673 and Macro-F1 from 0.642 to 0.666 through reporting-guided refinement. Compared with functionally equivalent custom implementations, NeuraGraph reduced implementation effort from 366 to 135 source code lines for relation extraction workflows while maintaining comparable extraction performance. The framework facilitates systematic comparison, reproducible evaluation, and rapid development of hybrid biomedical text mining pipelines.</p><p><strong>Availability and implementation: </strong>NeuraGraph is implemented in Python 3.12+ and released under the MIT license, with all dependencies listed in a requirements.txt file for straightforward installation. Source code and documentation are available at https://github.com/tyrone1979/neuragraph. The software runs natively on Linux, macOS, and Windows.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag240"},"PeriodicalIF":2.6,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921893","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":"BiDRA: Bayesian inference as a robust alternative to non-linear regression for dose-response efficiency metrics assessment.","authors":"Caroline Labelle, Petr Smirnov, Maud David, Mario Callejo, Benjamin Haibe-Kains, Anne Marinier, Sébastien Lemieux","doi":"10.1093/bioadv/vbag245","DOIUrl":"https://doi.org/10.1093/bioadv/vbag245","url":null,"abstract":"<p><strong>Motivation: </strong>Dose-response metrics such as the half-maximal inhibitory concentration, high-dose-response, and slope are central to drug discovery, yet standard Levenberg-Marquardt fits often produce biased or unsupported values for incomplete curves and lack uncertainty measures. In practice, this forces experimenters to visually inspect each curve fit to judge its reliability, a tedious, subjective, and non-scalable process.</p><p><strong>Results: </strong>Across 421 405 public dose-response experiments from three large pharmacogenomic datasets, shared-concentration viability responses were highly consistent across biological replicates, making it reasonable to expect derived efficiency metrics to show comparable replicate behavior when supported by the data. However, Levenberg-Marquardt fits to incomplete curves often produced unsupported metric values, including inflated rates of observable potency estimates (>70% versus ∼39% complete curves), which could falsely suggest metric-level disagreement between similar replicate responses. BiDRA represents efficacy, potency, and slope as posterior distributions, allowing uncertainty to remain large when the data do not support precise metric inference. This avoids treating uncertainty-dominated responses as conflicting point estimates, exposes unsupported Levenberg-Marquardt estimates, and supports uncertainty-aware compound ranking. We illustrate its utility in a structure-activity relationship screen, demonstrating how posteriors enable robust, criteria-based selection in drug discovery settings.</p><p><strong>Availability and implementation: </strong>BiDRA is implemented in Julia and available at https://github.com/lemieux-lab/bidra_robustness.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag245"},"PeriodicalIF":2.6,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119213","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":"ZSCAN-DDIE: an interpretable zero-shot learning method for the prediction of drug-drug interaction events using biomedical text.","authors":"Shaoxi Gao, Zhanpeng Gan, Fangfang Han, Yongming Cai","doi":"10.1093/bioadv/vbag243","DOIUrl":"10.1093/bioadv/vbag243","url":null,"abstract":"<p><strong>Motivation: </strong>Unexpected drug-drug interaction events (DDIEs) pose substantial clinical risks, yet many remain unannotated due to data scarcity and the rapid emergence of novel drug combinations. Conventional deep learning approaches struggle to generalize to these unseen interaction types and often lack interpretability under severe class imbalance. To address these challenges, we propose ZSCAN-DDIE, an interpretable zero-shot learning framework for DDIE prediction. The model integrates a biomedical pre-trained language model with an attention-based graph convolutional network (AGCN) to encode DDIE textual semantics and drug molecular structures, respectively. A cross-attention network (CAN) is introduced to align molecular substructures with pharmacological semantic components, enabling fine-grained cross-modal reasoning and improving interpretability at the substructure level. To mitigate modality bias and long-tailed distribution effects, we design a bimodal dynamic alignment (BDA) loss that combines hyperspherical embedding regularization with a stage-adaptive loss-switching mechanism. Experimental results under both conventional and generalized zero-shot settings demonstrate that ZSCAN-DDIE consistently outperforms state-of-the-art baselines across multiple evaluation metrics. The proposed framework not only enhances prediction accuracy for unseen DDIE categories but also provides biologically meaningful insights into molecular interaction mechanisms, offering a robust and clinically relevant solution for pharmacovigilance and drug safety assessment.</p><p><strong>Availability and implementation: </strong>The source code and data are available at https://github.com/GSX-0429/ZSCAN-DDIE.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag243"},"PeriodicalIF":2.6,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13570678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957337","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":"AINR: attention-guided implicit neural representations for spatial domain identification in spatial transcriptomics.","authors":"Yusen Zhang, Guodong Xiao, Ponian Li, Jian Liu","doi":"10.1093/bioadv/vbag241","DOIUrl":"10.1093/bioadv/vbag241","url":null,"abstract":"<p><strong>Motivation: </strong>Spatial transcriptomics measures gene expression together with spatial locations, but its data are noisy and sparse, and existing graph-based methods are complex and hard to scale.</p><p><strong>Results: </strong>We present AINR, an end-to-end deep learning framework that models spatial transcriptomics data as a geometrically constrained continuous biological field. AINR combines implicit neural representations with a spatially-aware attention mechanism and a total variation regularization term, using a periodic sine activation function to map spatial coordinates directly to gene expression while preserving spatial smoothness without explicit adjacency matrices. Across six diverse datasets, AINR consistently outperforms existing methods in spatial domain identification and remains robust even under extreme data sparsity.</p><p><strong>Availability and implementation: </strong>The code for AINR is available at https://github.com/XGD1122/AINR.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag241"},"PeriodicalIF":2.6,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13590037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149013257","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":"<b>pared</b>: model selection using multi-objective optimization.","authors":"Priyam Das, Sarah Robinson, Christine B Peterson","doi":"10.1093/bioadv/vbag211","DOIUrl":"10.1093/bioadv/vbag211","url":null,"abstract":"<p><strong>Motivation: </strong>Model selection is a ubiquitous challenge in statistics. For penalized models, model selection typically entails tuning hyperparameters to maximize a measure of fit or minimize out-of-sample prediction error. However, these criteria fail to reflect other desirable characteristics, such as model sparsity, interpretability, or smoothness.</p><p><strong>Results: </strong>We present the R package pared for Pareto-based model selection using multi-objective optimization. The package provides a generic interface, pared_optimize(), that allows users to define model-specific tuning parameters, objective functions, and objective directions. Built-in wrappers are also provided for the elastic net, fused lasso, fused graphical lasso, and group graphical lasso. Gaussian process-based multi-objective optimization is used to approximate the Pareto front, and interactive graphics allow users to inspect trade-offs among fit, sparsity, smoothness, structural similarity, and other user-defined criteria.</p><p><strong>Availability and implementation: </strong>The pared R package and vignettes are available at https://github.com/priyamdas2/pared. The archived release corresponding to this manuscript is available on Zenodo with DOI: 10.5281/zenodo.20533535.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag211"},"PeriodicalIF":2.6,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815131","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":"metadeconfoundR: Covariate analysis of high-dimensional cross-sectional omics data.","authors":"Till Birkner, Chia-Yu Chen, Morgan Essex, Kilian Dahm, Ulrike Löber, Thomas Ulas, Víctor Hugo Jarquín-Díaz, Sofia Kirke Forslund-Startceva","doi":"10.1093/bioadv/vbag242","DOIUrl":"https://doi.org/10.1093/bioadv/vbag242","url":null,"abstract":"<p><strong>Motivation: </strong>Identifying disease biomarkers from large molecular datasets is complicated by correlated and confounded signals like comorbidities and treatment regimens, batch effects, and cohort biases. These effects bias statistical inference and clinical conclusions. Robust methodologies are fundamental for reliable biomarker discovery.</p><p><strong>Results: </strong>metadeconfoundR is an R package for conservative biomarker discovery in (multi-)omics case-control datasets. It has a scalable two-step confounder-aware statistical framework for retaining only associations with independent support. It identifies covariate-naive univariate associations between omics features and metadata, then re-evaluates these associations using parallel post-hoc nested linear model testing to account for potential confounders. Confounded associations are flagged if they fully reduce to at least one other variable. metadeconfoundR supports parallel computation for large-scale datasets, offers visualization and tools for interpreting results and secondary analyses. We benchmark metadeconfoundR against state-of-the-art methods for identifying biomarkers using simulated ground truth derived from microbiome data, and demonstrate its ability to disentangle confounding effects while preserving statistical power, offering particular advantage when multiple covariates are present. metadeconfoundR functions for any -omics data type with continuous or categorical metadata/covariates.</p><p><strong>Availability: </strong>metadeconfoundR is available on CRAN (https://cran.r-project.org/web/packages/metadeconfoundR/) and GitHub (https://github.com/TillBirkner/metadeconfoundR).</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag242"},"PeriodicalIF":2.6,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13581263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992694","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":"Inductive bias influences the spatial scale of biological features learned from images.","authors":"Jacob I Evarts, Jason Y Cain, Po-Hao Chiu, Ian Jan, Nancy L Allbritton, Neda Bagheri","doi":"10.1093/bioadv/vbag238","DOIUrl":"10.1093/bioadv/vbag238","url":null,"abstract":"<p><strong>Motivation: </strong>The inductive bias of a deep learning model influences the features it extracts from biological images, making model selection a critical scientific decision. We systematically compare representations learned from scratch without pre-training by convolutional neural networks (CNNs), vision transformers, and Fourier neural operators (FNOs)-an emerging architecture largely unexplored in biological imaging-to determine how their distinct learning mechanisms shape feature learning from spatiotemporal data.</p><p><strong>Results: </strong>Using a self-supervised representation learning framework on microscopy images of 2D gastruloids, as well as a distinct synthetic tumor image dataset, we show that while CNNs and FNOs achieve comparable accuracy on biological tasks, they learn features at different scales. Visual interpretation methods reveal that CNNs prioritize local, fine-grained details, whereas FNOs capture global, low-frequency structures, in line with overall population morphology. These findings demonstrate that deep learning model architecture is a choice that shapes the biological scale of extracted features, highlighting the need to align a model's inductive bias with the scientific question.</p><p><strong>Availability and implementation: </strong>All source code for the representation learning model, the tumor simulation dataset, and the gastruloid microscopy imaging dataset are available at the corresponding DOIs: https://doi.org/10.5281/zenodo.19672838, https://doi.org/10.5281/zenodo.19361370, https://doi.org/10.5281/zenodo.19373112, respectively.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag238"},"PeriodicalIF":2.6,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13549382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908206","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":"DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.","authors":"Wai-Kai Tsui, You-Xiang Chan, Kin-Hung Chow, Pak-Leung Ho, Huiluo Cao","doi":"10.1093/bioadv/vbag237","DOIUrl":"10.1093/bioadv/vbag237","url":null,"abstract":"<p><strong>Motivation: </strong>Virulence factors (VFs) play critical roles in bacterial pathogenesis, and identifying these proteins and elucidating their mechanisms is essential for developing effective infection treatments. While protein language models (PLMs) have revolutionized the protein analysis, current <i>in silico</i> methods for classifying VFs primarily rely on fine-tuned PLMs ProtBert-BFD that perform identification and classification in a single step. This monolithic approach lacks flexibility and is vulnerable to error propagation, particularly from false negatives. Moreover, the direct use of PLM embeddings as features for VF prediction and classification remains underexplored.</p><p><strong>Results: </strong>To overcome these limitations, we introduce the Deep learning VF Identifier and Classifier (DeepVIC), a modular framework that separates identification and classification into distinct states. The identification module employs a binary classifier using information-rich PLM embeddings. The classification module then integrates these embeddings with evolutionary features derived from position-specific scoring matrices (PSSMs) in a multiclass classifier, structured according to the virulence factor database (VFDB) schema. We benchmarked DeepVIC against six state-of-the-art VF classifiers and predictors using a large independent holdout dataset and two literature-curated datasets for <i>Streptococcus pneumoniae</i> and <i>Lactococcus</i> spp. DeepVIC demonstrated strong generalizability, achieving robust and balanced performance on the independent holdout dataset and competitive recall of putative VFs from existing literature. The modular, task-separation architecture adopted in DeepVIC offers exceptional flexibility, making it a uniquely adaptable and competitive framework. Model interpretability strategies reveal that DeepVIC leverages biologically relevant motifs and effectively integrates PLM embeddings with evolutionary features, offering insights into how deep learning models interpret VFs.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag237"},"PeriodicalIF":2.6,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889613","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":"Interpretable prediction of nucleic acid-binding proteins using a protein language model.","authors":"Hanjin Kim, Sung-Gwon Lee, Jooseong Oh, Kee K Kim, Eun-Mi Kim, Chungoo Park","doi":"10.1093/bioadv/vbag235","DOIUrl":"10.1093/bioadv/vbag235","url":null,"abstract":"<p><strong>Motivation: </strong>Accurate identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) is critical for elucidating transcriptional and post-transcriptional regulatory mechanisms. However, existing computational approaches often rely on inferred labels or domain-specific annotations, which limit the subsequent generalizability.</p><p><strong>Results: </strong>This study aimed to introduce transformer-based classifiers for human DBPs and RBPs that rely solely on protein sequence information without engineered features or domain constraints. The models were implemented using ESM-2 with low-rank adaptation (LoRA) fine-tuning and trained on experimentally validated datasets, including chromatin immunoprecipitation sequencing (ChIP-seq) annotations for DBPs and eCLIP annotations for RBPs. Next, to evaluate biological relevance, we computed value-aware attention (VAT) scores aggregated across transformer layers to interpret model focus. In 20-fold cross-validation, the DBP model achieved an area under the receiver operating characteristic curve (AUROC) of 0.84 with a Matthews correlation coefficient (MCC) of 0.40, while the RBP model achieved an AUROC of 0.92 with an MCC of 0.46. Proteins predicted as nucleic acid-binding were enriched for known binding domains, and inspection of attention distributions revealed preferential focus on annotated functional regions rather than non-binding segments. These results demonstrate that attention-based protein language models can accurately identify nucleic acid-binding proteins directly from sequence data. Moreover, these models reveal biologically meaningful sequence determinants of binding, establishing an interpretable and scalable framework for proteome-wide characterization of protein-nucleic acid interactions.</p><p><strong>Availability and implementation: </strong>Code is available on GitHub (https://github.com/CSB-hub/DRBP).</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag235"},"PeriodicalIF":2.6,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908268","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":"T-SMmOTE: tweaked synthetic majority minority oversampling technique for data scarcity issue in multi omics studies.","authors":"Payel Sadhukhan, Animesh Acharjee","doi":"10.1093/bioadv/vbag236","DOIUrl":"10.1093/bioadv/vbag236","url":null,"abstract":"<p><strong>Motivation: </strong>Multiomics data offer a rich data mine for modeling complex as well as day-to-day diseases, but their practical deployment is constrained by the limited sample availability. To this end, generating synthetic samples is a viable remedy. Extant schemes operating along this line, however, are mostly limited to augmenting the minority class in imbalanced datasets and often produce synthetic samples that lack sufficient diversity and fail to faithfully capture the underlying data distribution. As a result, the full potential of synthetic augmentation in multi-omics learning remains underexplored. The aim is to address the data scarcity problem in multi-omics domain. We propose a synthetic oversampling framework, which is dedicated to addressing overall data scarcity in multi-omics datasets and the lack of diversity in synthetic samples. Contrary to conventional methods that restrict augmentation to minority classes and rely on interpolation of two neighbors, our method generates diverse yet distribution-aligned synthetic samples by interpolating three neighbors and extends this augmentation paradigm to the majority class. The framework first balances the dataset by generating synthetic minority samples, and subsequently augments the balanced dataset by oversampling both majority and minority classes.</p><p><strong>Results: </strong>Empirical evaluation on multi-omics data obtained from three heterogeneous health scenarios-inflammatory bowel disease, multi-organ dysfunction syndrome, and colorectal cancer-substantiates the utility of the proposed scheme in improving the predictive performance. The models trained on T-SMmOTE-augmented data achieve higher Matthews correlation coefficient values, along with improved <math> <mrow> <mrow> <msub><mrow><mi>F</mi></mrow> <mn>1</mn></msub> </mrow> </mrow> </math> scores for both majority and minority classes. Notably, oversampling of the majority class improves the cognition of the minority class as well. We also explore the consistency of the class distributions between the original and augmented class-specific datasets. These findings confirm the capability of our scheme to learn from small, high-dimensional multi-omics datasets and highlight its potential for non-invasive disease detection.</p><p><strong>Availability and implementation: </strong>https://github.com/payelu/TSMm.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag236"},"PeriodicalIF":2.6,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921872","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}