Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706635
Zhihao Yang, Yuan Lin, Jiajin Wu, Nan Tang, Hongfei Lin, Yanpeng Li
Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus.
{"title":"Ranking SVM for multiple kernels output combination in protein-protein interaction extraction from biomedical literature","authors":"Zhihao Yang, Yuan Lin, Jiajin Wu, Nan Tang, Hongfei Lin, Yanpeng Li","doi":"10.1109/BIBM.2010.5706635","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706635","url":null,"abstract":"Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130437542","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706599
Wei Kong, Xiaoyang Mou, Xiaohua Hu
Unsupervised machine learning approaches are efficient analysis tools for DNA microarray technique which can accumulate hundreds of thousands of genes expression levels in a single experiment. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF) are explored to identify significant genes and related pathways in microarray gene expression dataset. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles. By combining the significant genes identified by both ICA and NMF, the simulation results show great efficient for finding underlying biological processes and related pathways in Alzheimer's disease (AD) and the activation patterns to AD phenotypes.
{"title":"Exploring matrix factorization techniques for significant genes identification of microarray dataset","authors":"Wei Kong, Xiaoyang Mou, Xiaohua Hu","doi":"10.1109/BIBM.2010.5706599","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706599","url":null,"abstract":"Unsupervised machine learning approaches are efficient analysis tools for DNA microarray technique which can accumulate hundreds of thousands of genes expression levels in a single experiment. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF) are explored to identify significant genes and related pathways in microarray gene expression dataset. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles. By combining the significant genes identified by both ICA and NMF, the simulation results show great efficient for finding underlying biological processes and related pathways in Alzheimer's disease (AD) and the activation patterns to AD phenotypes.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132630345","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706621
Xiaofei Nan, Nan Wang, P. Gong, Chaoyang Zhang, Yixin Chen, D. Wilkins
Explosive compounds such as TNT and RDX have various toxicological effects on the natural environment. The goal of the earthworm microarray experiment is to unearth the biomarker for toxicity evaluation. We propose a novel recursive gene selection method which can handle the multi-class setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multi-class classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The empirical results demonstrate that the selected features (genes) have very competitive discriminative power. In addition, the selection process has fast rate of convergence.
{"title":"Gene selection using 1-norm regularization for multi-class microarray data","authors":"Xiaofei Nan, Nan Wang, P. Gong, Chaoyang Zhang, Yixin Chen, D. Wilkins","doi":"10.1109/BIBM.2010.5706621","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706621","url":null,"abstract":"Explosive compounds such as TNT and RDX have various toxicological effects on the natural environment. The goal of the earthworm microarray experiment is to unearth the biomarker for toxicity evaluation. We propose a novel recursive gene selection method which can handle the multi-class setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multi-class classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The empirical results demonstrate that the selected features (genes) have very competitive discriminative power. In addition, the selection process has fast rate of convergence.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"PP 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126437569","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706608
N. Song, Hong Yan
This paper presents a new technique for the detection of short exons in DNA sequences. In this method, we analyze the DNA propeller twist and bending stiffness using the autoregressive (AR) model. The linear prediction matrices for the two features are combined to find the same set of linear prediction coefficients, from which we estimate the spectrum of the DNA sequence and detect protein-coding regions based on the 1/3 frequency component. To overcome the non-stationarity of DNA sequences, we use moving windows of different sizes in the AR model. Experiments on the human genome show that our multi-feature based method is superior in performance to existing exon detection algorithms.
{"title":"Autoregressive modeling of DNA features for short exon recognition","authors":"N. Song, Hong Yan","doi":"10.1109/BIBM.2010.5706608","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706608","url":null,"abstract":"This paper presents a new technique for the detection of short exons in DNA sequences. In this method, we analyze the DNA propeller twist and bending stiffness using the autoregressive (AR) model. The linear prediction matrices for the two features are combined to find the same set of linear prediction coefficients, from which we estimate the spectrum of the DNA sequence and detect protein-coding regions based on the 1/3 frequency component. To overcome the non-stationarity of DNA sequences, we use moving windows of different sizes in the AR model. Experiments on the human genome show that our multi-feature based method is superior in performance to existing exon detection algorithms.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284699","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706552
David Becerra, A. Sandoval, Daniel Restrepo-Montoya, L. F. Niño
Protein structure prediction is one of the most important problems in bioinformatics and structural biology. This work proposes a novel and suitable methodology to model protein structure prediction with atomic-level detail by using a parallel multi-objective ab initio approach. In the proposed model, i) A trigonometric representation is used to compute backbone and side-chain torsion angles of protein atoms; ii) The Chemistry at HARvard Macromolecular Mechanics (CHARMm) function optimizes and evaluates the structures of the protein conformations; iii) The evolution of protein conformations is directed by optimization of protein energy contributions using the multi-objective genetic algorithm NSGA-II; and iv) The computation process is sped up and its effectiveness improved through the implementation of an island model of the evolutionary algorithm. The proposed model was validated on a set of benchmark proteins obtaining very promising results.
{"title":"A parallel multi-objective ab initio approach for protein structure prediction","authors":"David Becerra, A. Sandoval, Daniel Restrepo-Montoya, L. F. Niño","doi":"10.1109/BIBM.2010.5706552","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706552","url":null,"abstract":"Protein structure prediction is one of the most important problems in bioinformatics and structural biology. This work proposes a novel and suitable methodology to model protein structure prediction with atomic-level detail by using a parallel multi-objective ab initio approach. In the proposed model, i) A trigonometric representation is used to compute backbone and side-chain torsion angles of protein atoms; ii) The Chemistry at HARvard Macromolecular Mechanics (CHARMm) function optimizes and evaluates the structures of the protein conformations; iii) The evolution of protein conformations is directed by optimization of protein energy contributions using the multi-objective genetic algorithm NSGA-II; and iv) The computation process is sped up and its effectiveness improved through the implementation of an island model of the evolutionary algorithm. The proposed model was validated on a set of benchmark proteins obtaining very promising results.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117227189","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706531
Yiwei Zhang, Fei Hu, Jijun Tang
Reconstructing phylogenies from gene-order data has become very attractive in the research of evolution these years. So far, most methods can only treat genomes with equal gene contents with each gene appearing exactly once in each genome. In this paper, we propose a new distance measurement for genomes with inversions and insertions/deletions that comply with triangle inequality. Based on this distance, we develop a new method to solve the median problem of unequal gene content, which are used to reconstruct both phylogenies and ancestral genomes. We test our method on simulated datasets under various conditions and the experimental results show that our distance measurement can produce more accurate phylogenetic trees compared with other popular methods for unequal genomes. Also our median algorithm produces remarkably more accurate ancestral genomes than the only unequal genome median solver that is currently available.
{"title":"Phylogenetic reconstruction with gene rearrangements and gene losses","authors":"Yiwei Zhang, Fei Hu, Jijun Tang","doi":"10.1109/BIBM.2010.5706531","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706531","url":null,"abstract":"Reconstructing phylogenies from gene-order data has become very attractive in the research of evolution these years. So far, most methods can only treat genomes with equal gene contents with each gene appearing exactly once in each genome. In this paper, we propose a new distance measurement for genomes with inversions and insertions/deletions that comply with triangle inequality. Based on this distance, we develop a new method to solve the median problem of unequal gene content, which are used to reconstruct both phylogenies and ancestral genomes. We test our method on simulated datasets under various conditions and the experimental results show that our distance measurement can produce more accurate phylogenetic trees compared with other popular methods for unequal genomes. Also our median algorithm produces remarkably more accurate ancestral genomes than the only unequal genome median solver that is currently available.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127885658","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706532
Bing Han, Xue-wen Chen
Epistatic interactions play a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions showed that Markov Blanket-based methods are capable of finding SNPs (single-nucleotide polymorphism) that have a strong association with common diseases and of reducing false positives when the number of instances is large. Unfortunately, a typical SNP dataset consists of very limited number of examples, where current methods including Markov Blanket-based methods perform poorly. To address small sample problems, we propose a Bayesian network-based approach to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method significantly outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small.
{"title":"Detecting SNPs-disease associations using Bayesian networks","authors":"Bing Han, Xue-wen Chen","doi":"10.1109/BIBM.2010.5706532","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706532","url":null,"abstract":"Epistatic interactions play a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions showed that Markov Blanket-based methods are capable of finding SNPs (single-nucleotide polymorphism) that have a strong association with common diseases and of reducing false positives when the number of instances is large. Unfortunately, a typical SNP dataset consists of very limited number of examples, where current methods including Markov Blanket-based methods perform poorly. To address small sample problems, we propose a Bayesian network-based approach to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method significantly outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129522889","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706556
Alvaro J. González, Li Liao, Cathy H. Wu
We present a new computational method for predicting ligand binding sites in protein sequences. The method uses kernelbased canonical correlation analysis and linear regression to identify binding sites in protein sequences as the residues that exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure based functional classification of the proteins in the context of a functional family. We explore the effect of correlations among multiple positions in the sequences and show that their inclusion enhances the prediction accuracy significantly.
{"title":"Predicting ligand binding residues using multi-positional correlations and kernel canonical correlation analysis","authors":"Alvaro J. González, Li Liao, Cathy H. Wu","doi":"10.1109/BIBM.2010.5706556","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706556","url":null,"abstract":"We present a new computational method for predicting ligand binding sites in protein sequences. The method uses kernelbased canonical correlation analysis and linear regression to identify binding sites in protein sequences as the residues that exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure based functional classification of the proteins in the context of a functional family. We explore the effect of correlations among multiple positions in the sequences and show that their inclusion enhances the prediction accuracy significantly.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131334226","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706628
Taehyong Kim, Jaehan Koh, Kang Li, M. Ramanathan, A. Zhang
Identification of a critical location in complex structures is one of the most important issues that affects the quality of safety in human life. Specifically, finding high fracture spots of a bone microstructure in our body is a very important research topic; however, it is still not well understood. In this paper, we study on identifying critical locations in a bone microstructure with our bone network model. In fact, about 25 million people in the United States suffer from osteoporosis, which is a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue leading to enhanced bone fragility and a consequent increase in fracture risk. However, currently available techniques for the diagnosis of osteoporosis and the identification of critical locations of bone microstructure are limited. We create a bone network model based on properties of a bone microstructure and we develop a method, called information propagation, to identify critical locations in a bone network. Our paper focuses on detecting important edges as critical locations for the strength of bone microstructure when there are external forces applied to the bone network. Then, we evaluate results of the method in comparison with existing methods including the weighted betweenness centrality and the weighted bridge coefficient. We conclude with the discussion on advantages and disadvantages among those methods.
{"title":"Identification of critical location on a microstructural bone network","authors":"Taehyong Kim, Jaehan Koh, Kang Li, M. Ramanathan, A. Zhang","doi":"10.1109/BIBM.2010.5706628","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706628","url":null,"abstract":"Identification of a critical location in complex structures is one of the most important issues that affects the quality of safety in human life. Specifically, finding high fracture spots of a bone microstructure in our body is a very important research topic; however, it is still not well understood. In this paper, we study on identifying critical locations in a bone microstructure with our bone network model. In fact, about 25 million people in the United States suffer from osteoporosis, which is a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue leading to enhanced bone fragility and a consequent increase in fracture risk. However, currently available techniques for the diagnosis of osteoporosis and the identification of critical locations of bone microstructure are limited. We create a bone network model based on properties of a bone microstructure and we develop a method, called information propagation, to identify critical locations in a bone network. Our paper focuses on detecting important edges as critical locations for the strength of bone microstructure when there are external forces applied to the bone network. Then, we evaluate results of the method in comparison with existing methods including the weighted betweenness centrality and the weighted bridge coefficient. We conclude with the discussion on advantages and disadvantages among those methods.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133695945","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}
Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706619
Wenan Chen, Charles Cockrell, Kevin Ward, K. Najarian
This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices, a feature selection scheme is applied to select the most informative features. Support vector machine (SVM) is used to train the data and build the prediction model. The validation is performed with 10 fold cross validation. To avoid overfitting, all the feature selection and parameter selection are done using training data during the 10 fold cross validation for evaluation. This results an nested cross validation scheme implemented using Rapidminer. The final classification result shows the effectiveness of the proposed method in ICP prediction.
{"title":"Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods","authors":"Wenan Chen, Charles Cockrell, Kevin Ward, K. Najarian","doi":"10.1109/BIBM.2010.5706619","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706619","url":null,"abstract":"This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices, a feature selection scheme is applied to select the most informative features. Support vector machine (SVM) is used to train the data and build the prediction model. The validation is performed with 10 fold cross validation. To avoid overfitting, all the feature selection and parameter selection are done using training data during the 10 fold cross validation for evaluation. This results an nested cross validation scheme implemented using Rapidminer. The final classification result shows the effectiveness of the proposed method in ICP prediction.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123704259","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：