Biodata Mining最新文献

{"title":"Quantum Angle-distance kernel for ECG classification and anomaly detection: a quantum-inspired framework for biomedical signal analysis.","authors":"Arman Salehi, Hamid Reza Goudarzi, Ashkan Heydarian","doi":"10.1186/s13040-026-00519-3","DOIUrl":"10.1186/s13040-026-00519-3","url":null,"abstract":"<p><p>Reliable, clear and lightweight ECG classification is important for immediate medical and wearable applications, yet challenges due to confounding signals, rare, labeled examples as well as high model complexity. Our method introduces a Quantum Angle–Distance Kernel (QADK), inspired by quantum principles, combining angular alignments in Hilbert space along with displacements relative to a reference point, thereby increasing class resolution without loss of transparency. We represent heartbeats using descriptive features rather than raw data; these are mapped into fixed-length complex vectors using a simulated quantum coding scheme. Comparisons between such encoded patterns use a balanced mixture of squared similarity scores and projection gaps, creating a stable kernel matrix that works directly with common learning tools. We test QADK on a large ECG heartbeat dataset using strict patient-wise splits on 100 Monte-Carlo measurements; comparisons include linear SVM, RBF SVM, 1D CNN, MLP, and single-class anomaly detection methods. During supervised classification, QADK-SVM achieves an average AUC-ROC of 0.977 ± 0.003, macro F1 of 0.923 ± 0.005, accuracy of 0.937 ± 0.004 – clearly beating linear SVM while matching the best performing RBF after embedding adjustment. In the single-class setting, QADK yields AUC = 0.884 ± 0.005 Ablation with nested cross-validation shows that both components contribute to performance in our pipeline: adding a reference term to the fidelity-only kernel resulted in an average pairwise increase in AUC of +2.4% points under the reported PTB distribution. The fidelity term primarily improved specificity in these experiments. Through sensitivity checks, we found that m = 32 offers a usable embedding size that preserves results without excessive computation time for upcoming quantum devices. Computational analysis shows that Gram construction is the main cost driver – scaling roughly as Θ(n2), yet remains manageable for medium-sized groups, especially when using low-level methods to increase efficiency. While all empirical validations in this study were performed using classical simulation, our theoretical resource analysis demonstrates that QADK is natively compatible with NISQ hardware, requiring only 11 qubits and standard swap tests. This confirms the framework’s suitability for a future shift from simulation to real device implementation. Unlike deep networks, QADK achieves, which combines strong classification accuracy, high interpretability, and compatibility with capacity-constrained systems, making it suitable for ECG tasks in wearable or resource-constrained clinical environments.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"14"},"PeriodicalIF":7.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146087722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis.","authors":"Ruba Fadul, Natnael Tumzghi, Mohamed Seghier, Fatma Al-Jasmi, Aamna AlShehhi","doi":"10.1186/s13040-026-00523-7","DOIUrl":"10.1186/s13040-026-00523-7","url":null,"abstract":"<p><p>BACKGROUND: Mucopolysaccharidoses (MPS) are a group of rare lysosomal storage disorders caused by enzyme deficiencies leading to the accumulation of glycosaminoglycans (GAGs). Their clinical heterogeneity and low prevalence contribute to delayed and often missed diagnoses. Early detection is critical for improving treatment outcomes. This study investigates the utility of Graph Neural Networks (GNNs) for diagnosing MPS using real-world electronic health records (EHRs). METHODS: Diagnostic features were extracted for 106 subjects (37 MPS and 69 controls) from the SEHA health system in Abu Dhabi. Four GNN architectures were trained and evaluated across seven feature selection strategies using nested stratified cross-validation. Model interpretability was assessed using Shapley Additive exPlanations (SHAP) to rank diagnostic features and PGExplainer to provide patient-level graph-topology explanations. RESULTS: The Graph Convolutional Network (GCN) combined with chi-square feature selection achieved the highest performance, with an AUC of 0.97 [95% CI: 0.93–1.00], sensitivity/specificity of 0.97/0.94, PPV/NPV of 0.9/0.98, F1-score of 0.93, and accuracy of 0.95. SHAP analysis highlighted clinically coherent diagnostic features aligned with the domain expert-driven features, while PGExplainer identified compact relational subgraphs that characterized MPS cases, complementing the global feature-level interpretation. CONCLUSION: The proposed graph-based diagnostic framework demonstrates strong potential for early MPS diagnosis using EHR data typically available in the clinical setting. By integrating high-performing predictive modeling with both global and patient-level interpretability, this approach provides a promising foundation for developing clinically meaningful and data-driven screening tools for rare diseases, particularly in the context of limited data availability. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"13"},"PeriodicalIF":7.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146067985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An online non-radiographic osteoporosis prediction calculator constructed using interpretable machine learning.","authors":"Yuqi Zhang, Sijin Li, Peibiao Mai, Qiang Su, Minnan Gao, Kuan Zeng, Chao Tong, Kun Zhang, Hui Huang","doi":"10.1186/s13040-026-00520-w","DOIUrl":"10.1186/s13040-026-00520-w","url":null,"abstract":"<p><p>BACKGROUND: Osteoporosis represents a major health challenge in aging populations, yet its diagnosis largely depends on dual-energy X-ray absorptiometry (DXA), which is both costly and radiation-based. This study aimed to develop a practical, non-radiographic prediction model for osteoporosis using interpretable machine learning techniques and to implement it as an accessible online calculator for rapid clinical and community screening. METHODS: Data were derived from the 2008–2011 waves of the Korean National Health and Nutrition Examination Survey (KNHANES). Individuals with over 30% missing data were excluded, and incomplete variables were imputed via polynomial interpolation (for continuous variables) and mode imputation (for categorical variables). After performing Spearman correlation analysis (p < 0.001) to identify osteoporosis-related features, GradientBoost-RFE and LASSO regression were applied for dimensionality reduction, yielding 15 essential predictors, including age, sex, body mass index (BMI), etc. GradientBoost, CatBoost, and XGBoost algorithms were trained to estimate abnormal DXA results and classify bone status. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), specificity (SPE), and accuracy (ACC), with a temporal validation set (the 2008 wave of KNHANES) for testing. RESULTS: A total of 18,179 participants were included, with 14,747 in the development cohort and 3,432 in the temporal validation set. Among them, 64.6% exhibited normal DXA results. The optimal model achieved an AUC of 0.845 and SPE of 0.897 for identifying abnormal DXA outcomes, and demonstrated an AUC of 0.876 and SPE of 0.909 in temporal validation. For multiclass classification (normal, osteopenia, osteoporosis), the model reached ACC of 0.724 and 0.744, and SPE of 0.803 and 0.819 in the development and validation datasets, respectively. CONCLUSION: We developed and validated an interpretable machine learning model that accurately predicts osteoporosis risk and DXA abnormalities using readily available demographic, biochemical, and lifestyle data. To facilitate clinical translation, the model has been deployed as an interactive online calculator, enabling non-invasive, rapid osteoporosis risk assessment without radiological testing. This tool may support early identification of high-risk individuals, optimize DXA utilization, and enhance preventive care strategies across diverse healthcare settings. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"11"},"PeriodicalIF":7.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12875019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep ensemble encoder network method for improved polygenic risk score prediction.","authors":"Okan Bilge Ozdemir, Raelynn Chen, Olivia Wu, Ruowang Li","doi":"10.1186/s13040-026-00521-9","DOIUrl":"10.1186/s13040-026-00521-9","url":null,"abstract":"<p><p>Genome-wide association studies of various heritable human traits and diseases have identified numerous associated single-nucleotide polymorphisms (SNPs), most of which have small or modest effects. Polygenic risk scores aim to better estimate individuals’ genetic predisposition by aggregating the effects of multiple SNPs from GWAS. However, current PRS is designed to capture only simple linear genetic effects across the genome, limiting their ability to fully account for the complex polygenic architecture. To address this, we propose Deep Ensemble Encoder Network (DEEN), a new method that ensembles autoencoders and fully connected neural networks to better identify and model linear and non-linear SNP effects across different genomic regions, improving its ability to predict disease risks. To demonstrate DEEN’s performance, we optimized the model across binary and continuous traits from the UK Biobank. Model evaluation on the held-out UK Biobank testing dataset, as well as the independent All of Us dataset, showed improved prediction and risk stratification, consistently outperforming other methods.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"9"},"PeriodicalIF":7.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrients.","authors":"Zhihui Xiong, Yi Yuan, Zhouhui Yun, Lijie Li, Yunmeng Chen","doi":"10.1186/s13040-025-00515-z","DOIUrl":"10.1186/s13040-025-00515-z","url":null,"abstract":"<p><p>OBJECTIVE: This study aimed to develop and validate a machine learning (ML)-based model for predicting the risk of gestational diabetes mellitus (GDM) using 45 dietary nutrients and baseline data. METHODS: A retrospective analysis was conducted on 3,649 pregnant women from the NHANES database (2007–2018). Baseline data (age, race/ethnicity, BMI, etc.) and 45 dietary nutrients were collected. The Synthetic Minority Oversampling Technique (SMOTE) was applied after the train-test split to address class imbalance. Feature selection used Variance Inflation Factor (VIF) to reduce multicollinearity and the Boruta algorithm to identify core predictors. Six ML models (XGBoost, LightGBM, RF, SVM, GNB, KNN) were trained. Performance was evaluated via AUC, accuracy, sensitivity, specificity, F-Beta score (β = 2), and PR-AUC. SHAP analysis clarified feature importance. RESULTS: Core predictors included race/ethnicity, BMI, protein, dietary fiber, α-carotene, β-carotene, lutein/zeaxanthin, folate (DFE), calcium, phosphorus, zinc, potassium, alcohol intake, educational level, and smoking status. XGBoost performed best in the validation set (accuracy: 93.1%, F-Beta: 0.943, AUC: 0.966, sensitivity: 97.5%, specificity: 86.7%, PR-AUC: 0.967), followed by LightGBM (accuracy: 92.6%) and RF (accuracy: 91.4%). GNB was poorest (accuracy: 57.3%, AUC: 0.658). SHAP identified educational level, race/ethnicity, lycopene, and smoking status as top contributors. CONCLUSION: ML models integrating demographics and 45 dietary nutrients accurately predict GDM. XGBoost, LightGBM, and RF excel, with XGBoost being most effective, supporting early GDM detection in clinical practice. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"12"},"PeriodicalIF":7.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models.","authors":"Muhammad Attique, Yaser Daanial Khan, Fahad Alturise, Tamim Alkhalifah","doi":"10.1186/s13040-025-00517-x","DOIUrl":"10.1186/s13040-025-00517-x","url":null,"abstract":"<p><p>RNA 5-hydroxymethylcytosine (5hmC) post-transcriptional modification plays an important role in the regulation of gene expression, its stability and evolution. These kinds of modifications emphasizes to understand their further biological impacts. Although several sequencing technologies supports in biochemical experiments for the precise determination of 5hmC modifications, these experimental identifications are laborious and costly; therefore, it is obvious to prioritise some proper and effective computational support. In this work, we introduce hm5C-DeepPred, which is a deep learning-based model that incorporates composite features including positional, relational and configurational properties of RNA sequences using moment based characteristic features. The model is predicated on a highly-tuned convolution neural network as its core predictor, complemented by fine-tuned transformer models as stronger comparative baselines. The generalizability and robustness of hm5C-DeepPred were verified by 10-fold cross-validation, statistical significance test, internal independent validation set and cross-species external testing. Our results showed that hm5C-DeepPred outperformed other predictors on those benchmarks, having the accuracy of 93.34% and MCC of 0.8942, respectively. We also used explainable AI techniques to interpret feature contributions and pinpoint the sequence elements that contributed most to model decisions. Hence, hm5C-DeepPred presents an effective and reliable computational tool that has potential to be highly reproducible for large-scale analysis of RNA 5hmC modifications.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"15"},"PeriodicalIF":7.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genotype subtyping approach to identify unnoticed variants in diseases from GWAS data.","authors":"Debora Garza-Hernandez, Emmanuel Martinez-Ledesma, Victor Trevino","doi":"10.1186/s13040-025-00512-2","DOIUrl":"10.1186/s13040-025-00512-2","url":null,"abstract":"<p><p>Genome-wide association studies (GWAS) are vital for investigating single-nucleotide polymorphisms (SNPs) in diseases. Comparisons of detected SNPs for the same disease between populations often reveal gaps, which are attributed to factors such as population, sample size, and rare variants, among others. We propose a method referred to as ‘Genotype Subtyping’ that is based on a classical GWAS to identify seed SNPs; each SNP stratifies cases and controls by its genotype, followed by a sub-GWAS per genotype while keeping the additional number of statistical tests low. We evaluated four databases and populations, including Crohn’s disease, schizophrenia, breast cancer, and type 2 diabetes. Our findings and simulations demonstrate that Genotype Subtyping identifies additional significant SNPs or highlights SNPs that increase their significance importantly in all tested diseases. Some of these SNPs have already shown associations with the disorders in other populations. This result suggests that additional associations not observed before can be potentially novel. For instance, we confirmed associations near CASC15 (rs7760611) with breast cancer and near NDUFAF4 (chr6:97339280) with schizophrenia, even though they initially fell short of genome-wide significance. Moreover, we noted a SNP near BUD13 (rs1263149) showing a potential link to type 2 diabetes. We demonstrate that Genotype Subtyping is highly sensitive and specific by positive and negative simulations. Our results underscore the reliability of Genotype Subtyping as a method for identifying and validating associations for specific phenotypes by reanalyzing GWAS data, ultimately facilitating target gene identification. The implementation is available in a GitHub repository ( https://github.com/vtrevino/Genotype_Subtyping ).</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"8"},"PeriodicalIF":7.9,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12875041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145949375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalization or mirage? Data leakage and reported performance in neonatal EEG seizure detection models: a systematic review.","authors":"Geletaw Sahle Tegenaw, Hailin Song, Tomas Ward","doi":"10.1186/s13040-025-00516-y","DOIUrl":"10.1186/s13040-025-00516-y","url":null,"abstract":"","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13339558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145935756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-cohort genetic risk prediction for Alzheimer's disease: a transfer learning approach using GWAS and deep learning models.","authors":"Isibor Kennedy Ihianle, Wathsala Samarasekara, Keeley Brookes, Pedro Machado","doi":"10.1186/s13040-025-00506-0","DOIUrl":"10.1186/s13040-025-00506-0","url":null,"abstract":"<p><p>Alzheimer’s Disease (AD) represents a growing global health challenge, driven by complex genetic factors and diverse risk contributors. Currently, an estimated 55 million people worldwide are affected by dementia, with AD responsible for 60–70% of these cases. This paper explores the application of advanced machine learning approaches to predict AD risk using Genome-Wide Association Studies data from multiple cohorts, with a particular focus on transfer learning and feature selection techniques. We evaluate the performance of Wide and Deep Neural Networks and Multi-Head Attention in assessing their ability to generalise across datasets. As part of this, we explore knowledge distillation as a strategy to enhance model efficiency through improved generalisation performance in smaller architectures by transferring knowledge from high-capacity models to lightweight ones. Furthermore, the performance of these deep learning approaches is compared with tree-based ensembles, including Random Forest and XGBoost. Our experiments evaluate the generalisability, transferability, and efficiency of these models across different transfer learning scenarios. Findings indicate that aggregating multi-cohort training data significantly enhances predictive performance, highlighting the importance of data diversity in improving AD risk assessment. The proposed knowledge distillation approach enables the transfer of knowledge from a complex teacher model to a simpler student model, significantly improving performance. To enhance interpretability, we apply SHAP (SHapley Additive exPlanations) to the student models, revealing cohort-specific differences in SNP importance and highlighting variants in genes such as ABI3BP and SYN3, both of which are linked to immune and synaptic functions in AD. The integration of SHAP enables transparent interpretation of model decisions and supports the identification of transferable genetic markers, reinforcing the clinical relevance of our framework in AD risk prediction.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"89"},"PeriodicalIF":7.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12752400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145811982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition.","authors":"Yujie Huo, Weng Howe Chan, Ahmad Najmi Bin Amerhaider Nuar, Hongyu Gao","doi":"10.1186/s13040-025-00498-x","DOIUrl":"10.1186/s13040-025-00498-x","url":null,"abstract":"<p><p>Early detection of depression is vital for public health, yet current multimodal methods often struggle with challenges such as modality incompleteness, semantic inconsistency, and emotional temporal fluctuation. To address these issues, this paper proposes SBT-Net, a novel Semantic-Bias-Trend guided framework for robust depression detection from audio and text data. The model integrates three innovative modules: a semantically guided cross-modal gating (SGCMG) mechanism that dynamically filters effective modality features based on global semantic cues, a bias-guided tensor product attention (BG-TPA) mechanism that enhances fine-grained fusion and alignment between modalities, and an emotion trend modeling (ETM) module that captures the temporal evolution of depressive emotional states.We evaluate SBT-Net using two widely adopted benchmark datasets: DAIC-WOZ, which contains 189 interview sessions, and EATD-Corpus, comprising 162 conversational samples. Experimental results show that SBT-Net achieves excellent performance in multiple indicators, including 93.0% accuracy, 0.93 F1 score, and 0.92 recall, all of which surpass the competitive baselines.Ablation studies further validate the individual and synergistic contributions of each proposed module.These findings highlight the potential of integrating semantic guidance, bias-aware fusion, and emotional trend modeling to advance multimodal depression detection solutions. The source code can be found at https://github.com/ghy-yhg/SBT-Net .</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"86"},"PeriodicalIF":6.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12723886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145811969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}