IEEE transactions on computational biology and bioinformatics最新文献_第8页

A Molecular Perceptron Based on DNA Circuit. 基于DNA电路的分子感知器。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3728103

Chengye Zou, Qiang Zhang, Yongwei Yang, Changjun Zhou

引用次数: 0

CSD-GCN: Cross-Modal Self-Distillation Graph Convolutional Network for Alzheimer's Disease Diagnosis. 面向阿尔茨海默病诊断的跨模态自蒸馏图卷积网络。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3716969

Junchang Xin, Jinying Tao, Qi Chen, Jiani Li, Mingcan Wang, Zhiqiong Wang

{"title":"CSD-GCN: Cross-Modal Self-Distillation Graph Convolutional Network for Alzheimer's Disease Diagnosis.","authors":"Junchang Xin, Jinying Tao, Qi Chen, Jiani Li, Mingcan Wang, Zhiqiong Wang","doi":"10.1109/TCBBIO.2026.3716969","DOIUrl":"10.1109/TCBBIO.2026.3716969","url":null,"abstract":"Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that demands more comprehensive and precise modeling to unravel its complex pathological mechanisms and multidimensional brain network abnormalities. However, current brain network analysis approaches often suffer from limitations such as single-modal data processing and shallow fusion strategies, which hinder their ability to capture the multi-scale connectivity patterns associated with AD effectively. To alleviate these limitations, a novel multi-modal fusion framework is proposed in this paper, namely a cross-modal self-distillation graph convolutional network (CSD-GCN). The framework can deeply integrate fMRI and DTI data to facilitate the systematic extraction of hierarchical features from multimodal brain networks. The architecture of CSD-GCN comprises three progressive layers: edge-to-edge (E2E), edge-to-node (E2N), and node-to-graph (N2G). These layers are designed to perform multi-scale feature extraction, capturing neurodegenerative patterns ranging from local connection strengths to global topological structures. Moreover, the model incorporates a cross-modal self-knowledge distillation strategy and a cross-attention mechanism to enhance inter-modal feature alignment and fusion. These components collectively improve the generalization ability and discriminative capability of the learned representations. Experimental evaluations show that CSD-GCN outperforms existing methods in binary and ternary classification tasks for AD, with ablation studies confirming the individual effect of each component within the proposed framework.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2014-2025"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148612416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Cancer Driver Genes Based on Vision Transformer and Hierarchical Feature Fusion Module. 基于视觉变换和层次特征融合模块的癌症驱动基因识别。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3714710

Gaoshi Li, Yuqing Mao, Haiyan Huang, Jingli Wu, Jiafei Liu, Haize Hu

{"title":"Identifying Cancer Driver Genes Based on Vision Transformer and Hierarchical Feature Fusion Module.","authors":"Gaoshi Li, Yuqing Mao, Haiyan Huang, Jingli Wu, Jiafei Liu, Haize Hu","doi":"10.1109/TCBBIO.2026.3714710","DOIUrl":"10.1109/TCBBIO.2026.3714710","url":null,"abstract":"Cancer is a global public health problem that poses a huge threat to human life and health. The identification of cancer driver genes helps to discover the intrinsic mechanisms of cancer occurrence and development. Existing deep learning-based methods for identifying cancer driver genes are mainly biased towards local structure reconstruction, limiting the global learning. In order to increase the identification capacity of cancer driver genes, in this study, a new cancer driver genes prediction method VGDriver using Vision Transformer(ViT) and hierarchical feature fusion module is proposed. Its steps are as follows: first, the gene expression, copy number aberrations, and single nucleotide variations data are used to construct a gene property feature matrix; second, a new hierarchical feature fusion module is designed, which can perform a two-pipeline fusion of multi-omics features to obtain a multi-source feature matrix by co-designing the graph convolutional networks block and the multilayer perceptron block; finally, the multi-source feature matrix is input into ViT to train and perform the task of predicting cancer driver genes. The results of comparative experiments on five different protein-protein interaction datasets for pan-cancer and eight single cancer types show that the VGDriver method is better compared to seven representative methods in the evaluation metrics AUC and AUPR.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2026-2037"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148475778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LOGIC: LLM-Driven Cross-Scale Feature Coupling for Drug-Disease Interaction Prediction. 逻辑：llm驱动的跨尺度特征耦合用于药物-疾病相互作用预测。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3724882

Yunfei He, Shikai Chen, Yuchen Zhao, Jingmiao Xu, Jie Ren, Yuling Li, Fei Yang, Yiwen Zhang

{"title":"LOGIC: LLM-Driven Cross-Scale Feature Coupling for Drug-Disease Interaction Prediction.","authors":"Yunfei He, Shikai Chen, Yuchen Zhao, Jingmiao Xu, Jie Ren, Yuling Li, Fei Yang, Yiwen Zhang","doi":"10.1109/TCBBIO.2026.3724882","DOIUrl":"10.1109/TCBBIO.2026.3724882","url":null,"abstract":"Predicting drug-disease interactions (DSI) is a pivotal task in computational drug discovery, aiming to identify potential therapeutic or adverse effects between drugs and diseases. Current methodologies primarily model on two levels of features: the macroscopic level, utilizing network topology, and the mesoscopic level, leveraging molecular-level features. While valuable, these approaches share a common shortcoming: they frequently fail to capture fine-grained, mechanistic interactions. This refers to the specific interplay between drug functional groups and disease symptoms that underpins pharmacological effects. To address this limitation, we propose LOGIC, a novel model for LLM-driven cross-scale feature coupling for DSI prediction. LOGIC comprehensively models drug and disease representations across micro-, meso-, and macro-scales. The key innovation of LOGIC lies in constructing a dictionary of functional groups and symptoms, and performing a simple and intuitive multi-hot encoding of drugs and diseases at the micro-scale, and in employing large language models (LLMs) to derive the meso-scale features of diseases without requiring additional domain knowledge. LOGIC mainly consists of four modules: (1) Drug-disease micro-scale feature learning; (2) Drug-disease meso-scale feature learning; (3) Drug-disease macro-scale feature learning; and (4) cross-scale feature coupling prediction, which integrates micro-, meso- and macro-scale features for both drugs and diseases, and employs the matrix multiplication operation to model fine-grained feature interactions in the dimension level for DSI prediction. Extensive experiments conducted on multiple datasets validate the effectiveness and scalability of LOGIC.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2208-2221"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOSAIC: MOtif Set With Maximal InfluenCe on Network. MOSAIC：对网络影响最大的MOtif集。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3713273

Murat Calis, Volkan Altuntas, Tamer Kahveci

{"title":"MOSAIC: MOtif Set With Maximal InfluenCe on Network.","authors":"Murat Calis, Volkan Altuntas, Tamer Kahveci","doi":"10.1109/TCBBIO.2026.3713273","DOIUrl":"10.1109/TCBBIO.2026.3713273","url":null,"abstract":"Biological networks, characterized by complex interactions among genes, proteins, and metabolites, are often modeled as graphs to study their organizational principles and dynamics. Network motifs-recurring, statistically significant subgraphs provide critical insights into the functional properties and structural organization of these networks. Existing research has explored various facets of motif detection, including enumeration, edge and node independence, dynamic updates, multi-layered networks, and stochastic settings. Although there have been studies in exploring the functionality of individual motif instances, a significant gap remains in understanding how collections of motif instances act as a group to influence the overall functionality of the network. In this paper, we aim to fill this gap. We model the influence of collections of motifs as a novel problem, which we call the Closest $k$-Motif Set Selection problem. We prove that this problem is NP-hard and develop MOSAIC (MOtif Set with mAximal InfluenCe), a novel greedy algorithm to address this problem. MOSAIC operates in two phases: an initialization phase for computing distances between motifs and nodes, and an update phase that incrementally selects motif instances to optimize their collective impact on the network. We prove that MOSAIC is efficient with a low degree polynomial time complexity. Our experimental results demonstrate that MOSAIC achieves optimal or near-optimal results, and scales to the entire human network efficiently. Our experiments on the human transcriptional regulatory network demonstrate that MOSAIC can identify Alzheimer's genes effectively, and select Alzheimer's genes that are missed by state-of-the-art node-based selection methods. This work advances our understanding of motif-based network analysis and opens new avenues for exploring the functional implications of network motifs.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"1941-1954"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cell-Fate Decisions Mediated by Bistability and Oscillations in the p53-AKT-Mdm2 Network. p53-AKT-Mdm2网络双稳定性和振荡介导的细胞命运决定。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3713926

Yunfeng Zhang, Conghua Wang

{"title":"Cell-Fate Decisions Mediated by Bistability and Oscillations in the p53-AKT-Mdm2 Network.","authors":"Yunfeng Zhang, Conghua Wang","doi":"10.1109/TCBBIO.2026.3713926","DOIUrl":"10.1109/TCBBIO.2026.3713926","url":null,"abstract":"The tumor suppressor p53 plays a central role in stress-response signaling and exhibits crosstalk with the AKT pathway through the AKT-Mdm2 axis. Although this coupling has been linked to cell-fate choices between survival and death, the dynamical basis of these transitions remains unclear. In this paper, we develop a delay-differential equation model of the p53-AKT-Mdm2 network to study how interlocked feedback loops, together with transcriptional-translational delay, shape p53 dynamics. Bifurcation analysis reveals three dynamical regimes that can be linked to distinct cellular outcomes: a stable low-p53 equilibrium (pro-survival), sustained p53 oscillations (repair-associated dynamics), and a stable high-p53 equilibrium (pro-death). We show that the positive feedback generated by p53-dependent inhibition of AKT and AKT-enhanced Mdm2 activity produces bistability within a certain range of total AKT levels. We further find that increasing the delay destabilizes the low-p53 equilibrium and induces oscillations through a Hopf bifurcation. For the baseline parameter setting, a further increase in delay suppresses the oscillatory state and drives the system toward the high-p53 attractor. These results indicate how AKT-dependent bistability and delay-controlled instability jointly organize state transitions in the p53-AKT-Mdm2 circuit.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2006-2013"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

scCGC²T: Curriculum-Guided Contrastive Co-Training Framework for scRNA-Seq Data Clustering. scCGC2T：课程导向的scRNA-Seq数据聚类对比协同训练框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3731145

Zhenqiu Shu, Rong Dong, Tianyan Xu, Hongbin Wang, Zhengtao Yu

{"title":"scCGC2T: Curriculum-Guided Contrastive Co-Training Framework for scRNA-Seq Data Clustering.","authors":"Zhenqiu Shu, Rong Dong, Tianyan Xu, Hongbin Wang, Zhengtao Yu","doi":"10.1109/TCBBIO.2026.3731145","DOIUrl":"10.1109/TCBBIO.2026.3731145","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity. Accurate scRNA-seq data clustering is crucial for cell type identification and subpopulation discovery. However, existing clustering methods are frequently affected by erroneous pseudo-labels generated through the static threshold, thus progressively accumulating errors during training. To address these challenges, in this paper, we propose a novel curriculum-guided contrastive co-training (scCGC$^{2}$T) framework for scRNA-seq data clustering. It designs a dual-branch architecture for cross-view contrastive co-training, thereby strengthening its representation ability for scRNA-seq data. Additionally, a progressive curriculum learning strategy is introduced to filter pseudo-labels dynamically. Specifically, high-confidence samples using the static threshold are applied to initial training, followed by guidance based on dynamic thresholding. Low-confidence samples are gradually incorporated into the training process, thereby mitigating the noise interference problem. Therefore, the proposed scCGC$^{2}$T method effectively mitigates error accumulation in the early training stage and greatly enhances the discriminative capability for complex cell subpopulations. Extensive experiments on several benchmarks demonstrate that the proposed method achieves superior clustering performance and strong generalization on scRNA-seq data clustering tasks.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2346-2359"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148915108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Graph Representation Learning From a Statistical Perspective for Generalizable and Interpretable Protein-Ligand Binding Affinity Prediction. 从统计学角度的层次图表示学习用于可推广和可解释的蛋白质配体结合亲和力预测。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3717609

Changming Yao, Shunfan Li, Shanghui Deng, Jiahe Jiang, Tianyi Wang, Chang Tang

{"title":"Hierarchical Graph Representation Learning From a Statistical Perspective for Generalizable and Interpretable Protein-Ligand Binding Affinity Prediction.","authors":"Changming Yao, Shunfan Li, Shanghui Deng, Jiahe Jiang, Tianyi Wang, Chang Tang","doi":"10.1109/TCBBIO.2026.3717609","DOIUrl":"10.1109/TCBBIO.2026.3717609","url":null,"abstract":"Protein-ligand binding affinity (PLA) prediction aims to guide rational drug design by estimating the strength of interaction. The effectiveness of the representation learning of protein and ligand is key to successful PLA prediction. To this end, attention mechanism, as a powerful architectural paradigm, has been introduced and gradually emerged as the prevailing approach. However, intuitively, the classical attention paradigm based on similarity does not fit the biological mechanisms relevant for binding. Worse still, the cooperative and antagonistic effects among multiple atoms are deliberately disregarded in the classical formulation of attention mechanisms. Consequently, the rigid transplantation of classical architectures substantially undermines the PLA prediction performance. To address these challenges, we employ a hierarchical statistical attention model (HISA). Specifically, HISA employs a statistical attention mechanism (SAM) based on non-similarity computation to fit the biological prior and perceive the relationship of multiple atoms. In addition, we optimize HISA by employing clustering, enabling hierarchical representations of biomolecules. Extensive experiments demonstrate that HISA achieves state-of-the-art performance on multiple PLA benchmarks while simultaneously exhibiting generalizability and interpretability.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2078-2090"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148612365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constraints on the Perfect Phylogeny Mixture Model and Their Effect on Reducing Degeneracy. 完美系统发育混合模型的约束及其对减少退化的影响。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-08-21 DOI: 10.1109/TCBBIO.2026.3726506

John Marangola, Azadeh Sheikholeslami, Jose Bento

{"title":"Constraints on the Perfect Phylogeny Mixture Model and Their Effect on Reducing Degeneracy.","authors":"John Marangola, Azadeh Sheikholeslami, Jose Bento","doi":"10.1109/TCBBIO.2026.3726506","DOIUrl":"10.1109/TCBBIO.2026.3726506","url":null,"abstract":"The perfect phylogeny mixture (PPM) model is useful due to its simplicity and applicability in scenarios where mutations can be assumed to accumulate monotonically over time. It is the underlying model in many tools [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] that have been used, for example, to infer phylogenetic trees for tumor evolution and reconstruction [7]. Unfortunately, the PPM model gives rise to substantial ambiguity- in that many different phylogenetic trees can explain the same observed data- even in the idealized setting where data are observed perfectly, i.e., fully and without noise. This ambiguity has been studied in this perfect setting [17], which proposed a procedure to bound the number of solutions given a fixed instance of observation data. Beyond this, studies have been primarily empirical. Recent work proposed adding extra constraints to the PPM model to tackle ambiguity, In this paper, we first show that the extra constraints of [11], called longitudinal constraints (LC), often fail to reduce the number of distinct trees that explain the observations. We then propose novel alternative constraints to limit solution ambiguity and study their impact when the data are observed perfectly. Unlike the analysis in [17], our theoretical results-regarding both the inefficacy of the LC and the extent to which our new constrains reduce ambiguity are not tied to a single observation instance. Rather, our theorems hold over large ensembles of possible inference problems. To the best of our knowledge, we are the first to study degeneracy in the PPM model in this ensemble based theoretical framework.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CL-Pore: Contrastive Learning for Accurate Nanopore Sequence Recognition. CL-Pore：精确纳米孔序列识别的对比学习。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-08-19 DOI: 10.1109/TCBBIO.2026.3725796

Xiaoyu Guan, Daoqiang Zhang

{"title":"CL-Pore: Contrastive Learning for Accurate Nanopore Sequence Recognition.","authors":"Xiaoyu Guan, Daoqiang Zhang","doi":"10.1109/TCBBIO.2026.3725796","DOIUrl":"10.1109/TCBBIO.2026.3725796","url":null,"abstract":"Nanopore sequencing represents a potent means for single-molecule biomolecular characterization, yet the precise identification of sequences from nanopore data remains a formidable challenge, attributed to the intrinsic noise and data complexity. This study introduces a cutting-edge contrastive learning-based methodology designed to significantly bolster the accuracy of nanopore sequence identification. By harnessing Oxford Nanopore Technologies (ONT) barcode data for initial pre-training, we designed a model referred to as CL-pore (Contrastive Learning for nanopore data). The efficacy of CL-pore is further enhanced through fine-tuning with downstream nanopore datasets. To augment the training process, we introduce a novel data augmentation strategy. This involves the inversion of signals, the strategic injection of noise and the cropping of signals, creating pairs of positive and negative samples that are instrumental for contrastive learning. Rigorous experimental assessments reveal that CL-pore markedly surpasses previous methods, setting a new benchmark for performance. It consistently outperforms existing state-of-the-art techniques in accuracy and robustness for nanopore sequence detection. The results underscore the power of contrastive learning to distill meaningful features from noisy nanopore data, thereby achieving unparalleled accuracy in sequence identification. This methodology not only advances the potential of nanopore sequencing technologies but also paves the way for their expanded use in genomic and molecular biology research, where accurate sequence detection is paramount.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0