Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.","authors":"Lydia R Sidarous, Mostafa S Ibrahim, Mohamed Elhadidy, Alaa Abouelfetouh, Nehal A Saif, Nirmeen Aboelnaga, Michael Shehat, Mena Youssef, Eman Badr","doi":"10.1007/s12539-026-00855-2","DOIUrl":"https://doi.org/10.1007/s12539-026-00855-2","url":null,"abstract":"<p><p>Staphylococcus aureus (S. aureus) is a well-recognized pathogen known for its multi-drug resistance and diverse virulence mechanisms. Its ability to grow biofilms on implanted medical devices enhances its antimicrobial resistance (AMR) and virulence. Despite its clinical relevance, the underlying genetic basis of S. aureus biofilm formation remains insufficiently characterized, particularly regarding key biofilm-associated genes (BAGs) and their regulatory contributions. This study presents a two-part integrative approach to identify genetic determinants of biofilm formation in 178 Egyptian, clinical S. aureus isolates. The framework integrates a genome-wide association study (GWAS) module with a learning-based classification module. GWAS was conducted using a linear mixed model, while logistic regression was the best-performing model in binary and multiclass classification. Integrating both modules, we identified 20 BAGs as promising determinants of biofilm formation. Protein-protein interaction network and pathway enrichment analyses revealed their involvement in biofilm-related pathways. Of the identified BAGs, nine genes have direct links to biofilm formation in S. aureus or other bacteria, while the rest are linked to AMR, nutrient acquisition, and cell division. This study presents a robust framework for biofilm genomics research, uncovering 20 candidate BAGs that span diverse biological functions and capture the multi-faceted nature of biofilm formation in S. aureus.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148584373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation.","authors":"Waqas Anwaar, Van Manh, Wufeng Xue, Dong Ni","doi":"10.1007/s12539-026-00862-3","DOIUrl":"https://doi.org/10.1007/s12539-026-00862-3","url":null,"abstract":"<p><p>Accurate segmentation of medical volumes in magnetic resonance imaging is essential for the exploration of organ structures. Despite the impressive performance of supervised learning in medical image segmentation, its reliance on the large availability of labeled datasets poses a challenge due to the expertise and effort required for data acquisition. Semi-supervised learning (SSL) methods extract additional information from unlabeled datasets without involvement of expert annotation, and many approaches have been proposed to assist with this task. However, they often yield satisfactory results in the central regions but do not perform well in the edge regions due to low contrast edges and ambiguous tissue intensities at the boundary. Meanwhile, standard loss functions are biased towards large regions and cause under-penalizing of boundary errors. In this paper, we propose a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures. Firstly, we design a mutual learning module with multiple decoders to obtain different predicted probabilities through feature perturbations. Secondly, we apply a novel consistency constraint between labeled and unlabeled data by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an edge-aware uncertainty loss. We evaluate the proposed framework on two publicly available cardiac datasets, including the automated cardiac diagnosis challenge (ACDC) dataset under a 2D setting and the left atrium (LA) dataset under a 3D setting, and compare it with seven recent state-of-the-art semi-supervised methods. Through extensive qualitative visualization and quantitative experiments under standard semi-supervised settings, we demonstrate the effectiveness of the proposed approach and its superior segmentation performance compared with existing methods. Furthermore, clinically relevant cardiac measurements derived from the segmentation results are evaluated to analyze the clinical relevance of the proposed framework. To support reproducibility and future research, our code is publicly available at: https://github.com/waqasanwaar/SSL4MIS_PMEUL .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148535778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction.","authors":"Xiaodan Wang, Hongjian Li, Jihong Wang","doi":"10.1007/s12539-026-00859-y","DOIUrl":"https://doi.org/10.1007/s12539-026-00859-y","url":null,"abstract":"<p><p>Drug-drug interactions (DDIs) can compromise therapeutic efficacy and patient safety, making accurate computational prediction highly important in drug discovery and clinical decision support. We propose Multi-GraphDDI, a structure-only framework that predicts DDIs without relying on external biological networks. In this model, three complementary molecular fingerprints, namely extended-connectivity fingerprints (ECFP4), PubChem fingerprints, and pharmacophore fingerprints, are encoded as three grayscale channels and fused into a single image representation, while a parallel branch transforms the two-dimensional molecular graph into a topology-aware embedding through a five-layer residual graph isomorphism network (GIN). A bidirectional feature-interaction module together with four-head cross-attention is then used to align the image-based and graph-based representations, and the fused features are further used to estimate interaction scores. On ChCh-Miner, ZhangDDI, and DeepDDI, Multi-GraphDDI achieved AUC/AUPR/F1 scores of 0.9986/0.9998/0.9730, 0.9858/0.9633/0.8855, and 0.9922/0.9920/0.9598, respectively, outperforming competing methods. These results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.","authors":"Chenhao Li, Chunyan Wei, Qijia Tian, Dechao Bu, Yang Ge, Mingdi Zhang, Xiaolin Hu","doi":"10.1007/s12539-026-00856-1","DOIUrl":"https://doi.org/10.1007/s12539-026-00856-1","url":null,"abstract":"<p><strong>Introduction: </strong>Breast cancer remains a major global health burden, underscoring the urgent need for reliable early detection strategies. Exosomes, as mediators of intercellular communication, have shown promise in early tumor screening through Raman spectroscopy and gene expression profiling in pancreatic and colorectal cancers. However, the application of exosomal gene expression profiles for breast cancer prediction remains largely unexplored.</p><p><strong>Methods: </strong>Exosomal mRNA profiles were obtained from exoRBase 3.0 (242 breast cancer, 244 healthy controls). Sample sex was inferred using XIST and UTY expression, yielding 337 female samples for analysis. A nested cross-validation framework (20 repetitions, fivefold) was implemented, with differential expression analysis and feature selection performed exclusively within each training fold to prevent information leakage. Ten machine learning classifiers were evaluated on an independent held-out test set. Model performance was assessed using accuracy, precision, recall, and F1-score.</p><p><strong>Results: </strong>Feature selection demonstrated high stability (average Jaccard score 0.5912), with 9 genes consistently selected across all 100 iterations and a set of robust feature genes was identified. Among classifiers, xgbTree achieved the best performance (AUC 0.992, accuracy 0.970, F1 0.979) on the independent test set, supporting exosomal mRNA profiles as a promising non-invasive approach for the early breast cancer detection.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.","authors":"Yan Wang, Jiana Ding, Zhiyao Han, Chenxu Si, Yunzhi Liu, Nan Sheng, Lan Huang","doi":"10.1007/s12539-026-00852-5","DOIUrl":"https://doi.org/10.1007/s12539-026-00852-5","url":null,"abstract":"<p><p>Combination drug therapy is an effective approach to combating drug resistance and enhancing therapeutic efficacy in complex diseases such as cancer. Nevertheless, discovering synergistic drug pairs remains difficult because of the enormous combinatorial possibilities and the context-dependent, nonlinear interactions among drugs and cellular systems. Although recent computational methods have advanced drug synergy prediction, they often fail to preserve pharmacologically meaningful molecular substructures, capture global properties of complex molecular graphs, or model the varying importance of genes across cellular contexts. In this study, we propose GATESynergy, a novel deep learning framework for predicting drug synergy that combines a hierarchical gated gene-aware encoder (HiGate) with a molecular global-local aggregator (MoGLA). Specifically, MoGLA integrates local graph message-passing with global self-attention to jointly model functional motifs and long-range structural dependencies, overcoming the inability of conventional GNNs to simultaneously preserve pharmacologically meaningful substructures and capture global molecular topology. HiGate employs a residual gene-wise gating mechanism that adaptively weights genes according to their contextual relevance, yielding context-specific cell-line representations that address the neglect of gene-level importance in existing encoders. Furthermore, we propose a multi-head interactive additive attention module that uses global query summarization to efficiently fuse drug-drug-cell line representations and capture diverse synergistic interaction patterns. Extensive benchmarking results demonstrate that GATESynergy consistently surpasses current state-of-the-art methods. Moreover, a case study on 42 FDA-approved drugs further validates the effectiveness of GATESynergy in discovering novel synergistic drug combinations. The source data and code are available at https://github.com/coding-in-github/GATESynergy .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148455418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying RNA ac⁴C Modification Sites via Pseudo-Nucleotide Fingerprint Encoding and Multi-Scale Feature Integration.","authors":"Yiming Wang, Fan Mo, Yun Sha, Zhenguo Wen, Chun Fang","doi":"10.1007/s12539-026-00853-4","DOIUrl":"https://doi.org/10.1007/s12539-026-00853-4","url":null,"abstract":"<p><p>RNA modifications, particularly N⁴-acetylcytidine (ac⁴C), play essential roles in gene regulation by influencing mRNA stability, translational efficiency, and cellular responses to stress. Aberrant ac⁴C modifications have been implicated in tumorigenesis and cancer progression, highlighting their potential as diagnostic biomarkers and therapeutic targets. However, existing computational approaches for predicting RNA modification sites often rely on fixed-scale features or shallow learning architectures, limiting their ability to model multiscale nucleotide interactions and long-range dependencies. To address these limitations, we introduce DFM-ac⁴C, a novel computational framework designed for the accurate prediction of ac⁴C modification sites. DFM-ac⁴C integrates pseudo-nucleotide fingerprint features derived from Kidera factors with global contextual embeddings generated by the Evo2 and DNAshape pretrained models. Leveraging an attention-based fusion architecture, the framework dynamically combines local sequence information, fingerprint descriptors, and global embeddings, enabling it to effectively capture the complex, multiscale characteristics of nucleotide interaction sites. Extensive performance evaluations demonstrate that DFM-ac⁴C significantly outperforms existing models, achieving outstanding predictive metrics, including an AUC of 96.76%, accuracy (ACC) of 90.49%, Matthew's correlation coefficient (MCC) of 0.8098, sensitivity (SEN) of 90.22%, and specificity (SPE) of 90.76%. These results underscore DFM-ac⁴C's effectiveness as a robust and efficient tool for RNA ac⁴C site identification. The complete dataset and source code supporting this study can be accessed freely from: https://github.com/BioAI-wym/DFM-ac4C .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148404515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.","authors":"Manyu Zheng, Ying Fang, Lijie Na, Qi Zhao","doi":"10.1007/s12539-026-00854-3","DOIUrl":"https://doi.org/10.1007/s12539-026-00854-3","url":null,"abstract":"<p><p>PIWI-interacting RNAs (piRNAs) are an important class of non-coding RNA molecules in epigenetic regulation. It plays a crucial role in maintaining genomic stability and inhibiting transposable elements, and have been proven to participate in various diseases by regulating gene expression and influencing signaling pathways. Traditional biological experimental methods have limitations such as low throughput, long cycles, and high costs, making them difficult to meet the requirements of large-scale systematic screening. In this study, we develop a predictive framework named PiDA-DVLSA. We integrate autoencoder, dual graph transformer, and multi-head self-attention mechanisms, and construct an end-to-end multimodal deep learning system. We use autoencoder to perform nonlinear dimensionality reduction and denoising on piRNA sequence features and disease phenotype semantic features, and extract potential representations with strong discriminative ability. Then, we use graph transformers to model the high-order topological relationships between nodes in isomorphic similar graphs, and input heterogeneous graph transformers to learn complex cross-entity interaction patterns in heterogeneous networks. Finally, we achieve adaptive fusion of multi-source information through multi-head self-attention mechanisms. PiDA-DVLSA performs excellently on the benchmark dataset, with AUC and AUPR reach 0.9437 and 0.9195, respectively, significantly outperform eight mainstream algorithms. In independent case validations for breast cancer, clioblastoma, and Alzheimer disease, our model successfully predicts multiple biologically significant potential associations, further confirming its practicality and effectiveness in real scientific research scenarios and providing a solid computational basis for future precision diagnostic and therapeutic applications. PiDA-DVLSA is freely available at https://github.com/zhaoqi106/PiDA-DVLSA .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.","authors":"Jin Xie, Junxiong Li, Yulong Wu, Jing Nie, Jian Hu, Yuansong Zeng","doi":"10.1007/s12539-026-00851-6","DOIUrl":"https://doi.org/10.1007/s12539-026-00851-6","url":null,"abstract":"<p><p>Drug-target binding affinity (DTA) is central to computer-aided drug design. Although biochemical assays yield accurate measurements, their high cost and inefficiency limit scalability. Computational approaches, particularly deep learning, have recently shown promising progress in accelerating DTA prediction and reducing experimental burden. However, existing methods rarely incorporate biochemical knowledge, which limits their performance. To address this issue, we propose a deep learning framework termed DTANet+ for drug-target affinity prediction, which is inspired by the biochemical properties of drugs and targets. Specifically, given that functional groups and peptide chains, which vary significantly in length, are crucial in determining drug and target properties, we propose a Kernel-diverse Feature Extraction Block (KFEB) and Cross-Scale Interaction Module (CSIM) to extract drug and target features. Inspired by the crucial role of drug-target binding sites in precise affinity prediction, we introduce the Drug-Target Interaction Module (DTIM) to explore drug-target relationships and focus on binding sites. Besides, we design a Multi-modal Fusion Module (MFM) to effectively integrate the complementary information across different modalities and conduct prediction. Extensive experiments on the KIBA and Davis datasets show that DTANet+ consistently outperforms existing methods, achieving higher Concordance Index (CI) and lower Mean Squared Error (MSE). These results confirm the effectiveness of DTANet+ and highlight its potential for drug repositioning and personalized medicine. The source code is publicly available at: https://github.com/202324131016T/DTANet_plus .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148271270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.","authors":"Qi Gao, Junliang Shang, Shasha Yuan, Feng Li, Juan Wang","doi":"10.1007/s12539-026-00832-9","DOIUrl":"https://doi.org/10.1007/s12539-026-00832-9","url":null,"abstract":"<p><p>Recent advances in spatial transcriptomics (ST) have enabled the extraction of gene expression patterns while retaining spatial context. Identifying spatial domains is crucial for ST research. However, most existing spatial domain recognition methods cannot capture more complex relationships between gene expression profiles and spatial information. To bridge this gap, we propose a novel self-supervised learning framework named STNMAE for identifying spatial domains using a neighbor-aware multi-view masked graph autoencoder. Specifically, to fully exploit the dependencies between local neighbor information and globally similar expressions, we first construct multiple neighbor views with distinct similarity measures based on the gene expression profiles and spatial information. Additionally, we utilize a feature-masked encoder to extract more expressive embeddings. Then, STNMAE learns multiple view-unique embeddings through a multi-view autoencoder. Furthermore, the framework also uses regularization techniques through the latent representation prediction module to avoid overfitting and reduce the direct effect of input features. We apply STNMAE on seven ST datasets with different resolutions across distinct platforms. Finally, extensive evaluation confirms STNMAE's superiority over current state-of-the-art methods, indicating a substantial improvement in ST data analysis.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148251440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.","authors":"Congjun Rao, Xiaolong Zhang, Jingyan Hou","doi":"10.1007/s12539-026-00857-0","DOIUrl":"https://doi.org/10.1007/s12539-026-00857-0","url":null,"abstract":"<p><p>With the rapid development of artificial intelligence and medical image analysis, MRI-based automated diagnosis has provided an effective approach for Alzheimer's disease (AD) assessment. To improve the performance of MRI-based AD classification, this study proposes an AD diagnosis model termed High-level CBAM-ResNet34. First, T1-weighted structural MRI data are preprocessed using a unified pipeline and further converted into two-dimensional slices for model training. Then, a ResNet34-based classification framework is constructed, in which the convolutional block attention module (CBAM) is introduced into the high-level feature stage to enhance discriminative feature representation. In addition, to better adapt to inter-dataset differences, the negative-class weight parameter is selected according to the empirical performance on each dataset during training. Experimental results on the ADNI dataset show that the proposed model achieved an AUC of 0.8757 and an accuracy of 0.8160, outperforming several representative comparison models in overall performance. Ablation and comparison experiments further verified the effectiveness of the proposed design, and external validation on the open access series of imaging studies 1 (OASIS 1) dataset demonstrated its generalization ability. These results indicate that the proposed model is effective for MRI-based AD diagnosis and provides a useful reference for computer-aided neuroimaging analysis.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148251513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}