Biodata Mining最新文献

{"title":"SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions.","authors":"Xin Feng, Cheng Gao, Sudan Bai, Jiaxin Zheng, Cuinan Yu, Kewei Li, Lan Huang, Bo Han, Tao You, Jun Zhang, Fengfeng Zhou","doi":"10.1186/s13040-026-00534-4","DOIUrl":"10.1186/s13040-026-00534-4","url":null,"abstract":"<p><p>Sequence-based analysis and prediction form a cornerstone of bioinformatics investigations of the sequence-structure-function paradigm across DNA, RNA, and proteins. The exponential growth in sequence data necessitates sequence encoding methods and advanced predictive models. This study introduces a comprehensive machine learning and deep learning platform SeqHIVE, which aims to streamline the development of prediction pipelines for nucleic acid and protein sequences. SeqHIVE is implemented as a graphical user interface (GUI)–driven platform, enabling intuitive construction, evaluation, and visualization of sequence-based predictive models. SeqHIVE integrates a broad spectrum of 19 biological sequence encoding algorithms, 5 feature selection algorithms, and 26 classifiers for the biological sequence prediction tasks. It automates the processes of sequence-based feature extraction, model construction, predictive performance assessment, statistical analysis, and data visualization. SeqHIVE is engineered to serve both expert bioinformatics researchers with extensive customizable options, and biologists through a user-friendly interface and an intuitive design process. The utility of SeqHIVE is exemplified through a case study on iLearnPlus DNA locus prediction task. The tool SeqHIVE, the documentation, and its source code are freely available at: https://healthinformaticslab.org/supp/ . and is also hosted on the GitHub repository: https://github.com/EienKune/SeqHIVE .</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13196234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655216","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":"Light-XAI: a CADx for explainable cervical cancer detection via attention-based lightweight convolutional neural networks and layer-wise feature fusion.","authors":"Omneya Attallah","doi":"10.1186/s13040-026-00540-6","DOIUrl":"10.1186/s13040-026-00540-6","url":null,"abstract":"<p><p>Cervical cancer, the fourth most common cancer, requires prompt identification for treatment. The Pap smear test is the standard for cervical cancer detection and diagnosis, but it is complicated and laborious, and its efficacy depends on medical professionals. Computer-aided detection and diagnosis (CADx) systems employ deep learning, especially convolutional neural networks (CNN), to improve the precision and efficiency of cervical cancer diagnosis. Nevertheless, conventional CNNs do not inherently utilise instinctive methods of evaluating the relevance of features, as a qualified medical professional would, and instead assess features more broadly. In addition, CNNs are extremely efficient at extracting spatially invariant local features. Self-attention mechanisms allow CNNs to prioritise semantically significant clinical feature regions, gather relevant information with long-distance connections, and could efficiently model global-level features. To this end, this study integrates the self-attention mechanism into a CNN architecture and presents “Light-XAI”, a comprehensive lightweight explainable AI (XAI) CADx system for cervical cancer classification from Pap smear images. The system employs combinations of lightweight CNNs and applies the local gradient-weighted class activation mapping (Grad-CAM) technique for the explainability of decisions made. Furthermore, deep features from each CNN trained with Pap smear images are derived from two deep layers opposing current CADxs which either obtain features from a single layer or perform end-to-end CNN classification. In addition, features extracted from both deep layers of self-attention CNN are combined and decreased using a feature reduction method. Furthermore, the attributes of the three self-attention CNN models are fused, and subsequently a feature selection method is employed to choose the most impactful features. The results demonstrate that Light-XAI has achieved an accuracy of 97.27% and 99.82% for the SIPaKMeD and Mendeley liquid-based cytology (LBC) datasets, respectively, suppressing most of the present CADx. This superior performance allows Light-XAI to serve as a powerful tool to categorise cervical cancer. In addition, it shows the potential for improving diagnostic precision, allowing for the adoption of advanced methods in the field of medical image analysis.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13085275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655186","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":"Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.","authors":"John Zobolas, Anne-Marie George, Alberto López, Sebastian Fischer, Marc Becker, Tero Aittokallio","doi":"10.1186/s13040-026-00546-0","DOIUrl":"10.1186/s13040-026-00546-0","url":null,"abstract":"<p><p>BACKGROUND: Accurate prediction of patient survival using high-dimensional multi-omics data requires effective feature selection methods that balance predictive performance, sparsity, and stability to enable reproducible and transparent prognostic biomarker discovery. Existing approaches often rely on ad hoc thresholds, limiting their reproducibility and clinical utility. METHODS: We developed a hybrid ensemble feature selection (hEFS) framework that integrates data subsampling with multiple prognostic models, combining embedded and wrapper-based strategies for survival prediction. Features are ranked using a voting-theory-inspired aggregation approach across models and subsamples, while the optimal feature subset is determined via Pareto front optimization, eliminating the need for user-defined thresholds. We benchmarked hEFS against conventional CoxLasso using 100 Monte Carlo cross-validation iterations on multi-omics datasets from three pancreatic ductal adenocarcinoma (PDAC) cohorts, assessing sparsity, stability, redundancy, discrimination, and computational cost. RESULTS: Across all cohorts, hEFS selected substantially fewer and more stable features than CoxLasso (~10 vs. ~60 per modality), with lower variance, while still maintaining comparable discrimination performance with clinical baselines (C-index ~0.54–0.60). Feature redundancy was low, and ensemble subsampling further improved stability. Prognostic performance depended mainly on modality choice, rather than integration model, with gene expression plus clinical variables achieving the highest discrimination power (C-index ~0.64). CONCLUSIONS: hEFS provides a fully automated, interpretable, and reproducible framework for multi-omics biomarker discovery in high-dimensional survival analysis. By producing sparse, yet stable biomarker panels without loss of predictive power, hEFS represents a practical tool for integrative multi-omics modeling and it is openly available in the mlr3fselect R package. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13188360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647315","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":"Benchmarking genomic foundation models for binary classification of gene fusion breakpoints from DNA sequences.","authors":"Radim Krupička, Mariana Komárková, Bohuslav Dvorský, Kateřina Kollinová, Ondřej Klempíř","doi":"10.1186/s13040-026-00553-1","DOIUrl":"10.1186/s13040-026-00553-1","url":null,"abstract":"<p><p>BACKGROUND: Gene fusions are critical drivers of oncogenesis and diagnostic biomarkers in various cancers. However, their detection from RNA or DNA sequencing, when performed using traditional analytical methods, encounters challenges related to sample quality, computational complexity, and noise. Although deep learning is more robust, it usually requires large labeled datasets and substantial training resources. Genomic foundation models (GFMs), which are pre-trained on pangenome-scale data, offer a promising solution to these issues. METHODS: This study presents the first comprehensive benchmark of four transformer-based GFMs, Nucleotide Transformer (NT), Evo2, HyenaDNA, and DNABERT2, for the classification of gene fusion breakpoints. Using the curated FusionAI dataset of ~ 52,000 sequences, we extracted embeddings from 10-kilobase-pair (kbp) DNA sequences surrounding fusion breakpoints. We evaluated the quality of these representations qualitatively using t-SNE visualization and quantitatively by training lightweight classifiers (Support Vector Machines and simple Neural Networks) on the fixed embeddings. RESULTS: NT achieved the best performance with an accuracy of 0.967 and an F1 score of 0.967. This result outperformed the dedicated deep learning baseline (FusionAI, with an accuracy of 0.894). Evo2 was the second-best performer (accuracy: 0.920), demonstrating robustness derived from evolutionary pretraining. Conversely, DNABERT2 failed to compete (accuracy 0.677–0.723). Furthermore, sample efficiency analysis revealed that NT required only ~ 2,600 samples to reach 95% of its peak performance, whereas the baseline required over 14,000 samples. CONCLUSIONS: These findings demonstrate that advanced GFMs, particularly the NT and Evo2 models, generate highly discriminative ‘out-of-the-box’ embeddings. These embeddings significantly outperform dedicated deep learning baselines while requiring a fraction of the training data and computational time. This suggests that GFMs could be a scalable, data-efficient way of developing precise genomic diagnostic tools, particularly for rare diseases. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13182013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147628636","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 peak-oriented diffusion model for high-fidelity electrocardiogram reconstruction from photoplethysmogram: development and usability study.","authors":"Hyun-Myung Cho, Sungmin Han, Joon-Kyung Seong, Inchan Youn","doi":"10.1186/s13040-026-00544-2","DOIUrl":"10.1186/s13040-026-00544-2","url":null,"abstract":"<p><p>BACKGROUND: Cardiovascular disease is a leading cause of death, accounting for nearly one-third of all fatalities worldwide. The electrocardiogram (ECG), an electrophysiological signal representing cardiac activity, is a widely used non-invasive method for diagnosing cardiovascular diseases. However, ECG has limitations for continuous monitoring due to sensor size requirements, including electrodes. The photoplethysmogram (PPG), an optical method measuring blood volume variations, offers a more convenient alternative but provides less detailed information about heart activity than ECG. We introduce a peak-oriented diffusion model for PPG-to-ECG reconstruction, utilizing a U-Net framework composed of an encoder and decoder. OBJECTIVE: We aim for the model to learn the peak differences between ECG and PPG by predicting R peaks from the systolic peaks of PPG, thereby preserving the main components of ECG signals. We evaluate the reconstructed ECG through clinical interpretation and assess it with noise input to verify model robustness under a daily environment. METHODS: An R peak predictor, connected to the U-Net bottleneck, estimates R peaks incorporated into the decoder. The R peak predictor enables the model to reconstruct the ECG from the PPG while learning peak differences characterized as pulse transit time. We trained and evaluated the model using the MIMIC-III, WESAD, and PPG-DaLiA datasets. We assessed the model performance using root-mean-squared error and Fréchet distance, along with the ECG interpretation. In addition, atrial fibrillation detection and heart rate estimation are performed to verify clinical use of the reconstructed ECG. RESULTS: There were 59,980,410 segmented samples from 5,888 patients, split into train and test sets based on each patient, with an 8:2 ratio. The proposed model achieved a root-mean-square-error of 0.220, a Fréchet distance of 6.456, an F1-score of 0.925 for atrial fibrillation detection, and a mean difference of −0.077 [−1.877, 1.723] beats/min for heart rate estimation. CONCLUSIONS: Our results showed that the peak-oriented diffusion model translates PPG into high-fidelity ECG for clinical applications. We believe our findings expand the usefulness of the AI-based model to mobile healthcare, which involves continuous ECG monitoring in daily life. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13214367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634875","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":"Metabolic reprogramming of tumor-associated macrophage clusters in ovarian cancer: a single-cell transcriptomic analysis and prognostic model.","authors":"Lin Qi, Li Li, Yinuo Liu, Wenshu Li, Yaru Ma, Manyin Zhao","doi":"10.1186/s13040-026-00542-4","DOIUrl":"10.1186/s13040-026-00542-4","url":null,"abstract":"<p><p>PURPOSE: The complex tumor microenvironment (TME) in ovarian cancer (OC), particularly the role of macrophage clusters, plays a pivotal role in driving cancer progression and determining patient prognosis. Metabolic reprogramming is a hallmark of tumorigenesis and is crucial in reshaping the TME and driving tumor progression. METHODS: Through a comprehensive single-cell RNA sequencing analysis of macrophage clusters in OC, this study integrated data from four independent datasets and identified hypermetabolic subpopulations within nine distinct mononuclear phagocyte clusters. RESULTS: The identified clusters were notably enriched in pathways related to the TCA cycle, glycolysis, and HIF-1 signaling, which are closely linked to tumor invasiveness and immune modulation. Transcriptional regulation analysis revealed key drivers of these metabolic changes, including ZNF574, BRCA1, and ATF5, which might be potential therapeutic targets. Cell-cell communication analysis demonstrated that hypermetabolic macrophage clusters engaged in extensive signaling with tumor epithelial cells, thereby influencing the immune TME and potentially contributing to immune evasion. A metabolism-related prognostic model was also established based on differentially expressed genes within hypermetabolic macrophages, which exhibited robust predictive power for overall survival in OC patients. CONCLUSIONS: Our findings reveal strong associations between metabolic reprogramming in tumor-associated macrophages and OC progression, suggesting their potential importance in shaping the TME. This study provides novel insights into possible therapeutic strategies and prognostic tools for OC, which warrant further functional validation. CLINICAL TRIAL NUMBER: Not applicable. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13173987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617094","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":"WebGWAS: a web server for instant GWAS on arbitrary phenotypes.","authors":"Michael Zietz, Undina Gisladottir, Kathleen LaRow Brown, Nicholas P Tatonetti","doi":"10.1186/s13040-026-00548-y","DOIUrl":"10.1186/s13040-026-00548-y","url":null,"abstract":"<p><p>Complex disease genetics is a key area of research for reducing disease and improving human health. Genome-wide association studies (GWAS) help in this research by identifying regions of the genome that contribute to complex disease risk. However, GWAS are computationally intensive and require access to individual-level genetic and health information, which presents concerns about privacy and imposes costs on researchers seeking to study complex diseases. Publicly released pan-biobank GWAS summary statistics provide immediate access to results for a subset of phenotypes, but they do not inform about all phenotypes or hand-crafted phenotype definitions, which are often more relevant to study. Here, we present WebGWAS, a new tool that allows researchers to obtain GWAS summary statistics for a phenotype of interest without needing access to individual-level genetic and phenotypic data. Our public web app can be used to study custom phenotype definitions, including inclusion and exclusion criteria, and to produce approximate GWAS summary statistics for that phenotype. WebGWAS computes approximate GWAS summary statistics very quickly (<10 seconds), and it does not store private health information. We also show how the statistical approximation underlying WebGWAS can be used to accelerate the computation of multi-phenotype GWAS among correlated phenotypes. Our tool provides a faster approach to GWAS for researchers interested in complex disease, providing approximate summary statistics in short order, without the need to collect, process, and produce GWAS results. Overall, this method advances complex disease research by facilitating more accessible and cost-effective genetic studies using large observational data.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13170032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610349","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":"Machine learning-based assessment of the healthy human gut mycobiota landscape using ITS1 DNA metabarcoding data.","authors":"Giuseppe Defazio, Erika Lorusso, Mariangela De Robertis, Tommaso Mello, Andrea Galli, Graziano Pesole, Bruno Fosso","doi":"10.1186/s13040-026-00532-6","DOIUrl":"10.1186/s13040-026-00532-6","url":null,"abstract":"<p><p>The human gut microbiome plays a critical role in maintaining host health and homeostasis, and current literature suggests a bidirectional relationship between microbiome ecology and host well-being. DNA metabarcoding has emerged as a powerful tool for investigating microbiome imbalances (i.e., dysbiosis). While the prokaryotic microbiome has been extensively studied, the fungal counterpart - or mycobiome - remains largely unexplored, despite its recognized role from the perinatal stage onward. Here, we present a comprehensive survey based on DNA metabarcoding analysis of approximately 1,500 publicly available ITS1 samples. This survey integrates conventional statistical approaches with Machine Learning (ML) methods coupled with explainable Artificial Intelligence (XAI). ML models successfully predicted host health status with accuracies exceeding 80%, and fungal genera such as Eurotium, Aureobasidium, Candida, and Cutaneotrichosporon emerged as key classification features. This study introduces a cutting-edge multiview analytical framework applied to publicly available mycobiome data, highlighting the potential of fungal community profiling as a non-invasive tool to support health diagnostics.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13159301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610394","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":"Machine learning analysis of Drosophila testis transcriptomic data reveals potential regulatory sequences.","authors":"Viktor Vedelek, Balázs Vedelek, Rita Sinka","doi":"10.1186/s13040-026-00552-2","DOIUrl":"10.1186/s13040-026-00552-2","url":null,"abstract":"<p><strong>Background: </strong>The number of accessible transcriptomic data is increasing rapidly due to advances in affordable sequencing technologies. There is also an improvement in the quality and resolution of gene expression maps, which was driven by the advent of single-cell technologies.</p><p><strong>Results: </strong>Here we present a method, where we integrate transcriptomic data from five different sources, including segmented, single cyst, and single-cell transcriptomic data from Drosophila testis to investigate the expression and accumulation of transcripts in this tissue. The analysis showed that the testis-specific genes have a characteristic profile in the testis, which is predictable using supervised machine learning algorithms (XGBoost). Moreover, dimension reduction with an unsupervised machine learning algorithm (t-SNE) followed by clustering (DBSCAN) of the genes revealed potential regulatory motifs shared by genes in the same group. This approach links the robustness of tissue expression data with the resolution of the single-cell techniques, while masking the weakness of each technique.</p><p><strong>Conclusions: </strong>The presented approach can be used to find similarly expressed genes and shared regulatory elements or new cell-specific transcripts that could have potential annotation benefits in further research.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13162424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595678","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":"CancerHubs Data Explorer: a web application for investigating mutation-enriched protein interaction hubs in human cancers.","authors":"Ivan Ferrari, Elisa Arsuffi, Stefano Biffo, Nicola Manfrini","doi":"10.1186/s13040-026-00539-z","DOIUrl":"10.1186/s13040-026-00539-z","url":null,"abstract":"","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582169","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}