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Peptide-major histocompatibility complex (MHC) binding is an important prerequisite event and has immediate consequences to immune response. Those peptides binding to MHC molecules can activate the T-cell immunity, and they are useful for understanding the immune mechanism and developing vaccines for diseases. Recently, researchers are interested in making prediction about binding affinity instead of differentiating the peptides as binder or non-binder. In this paper, we use sparse Bayesian regression algorithm proposed by Tipping [M.E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. (2001)] to derive position-specific scoring matrices from allele-related peptides, and develop the models allowing for the prediction of MHC-II binding affinity. We explore the peptide length and peptide flanking residue length's impact on binding affinity, and incorporate these factors into our models to enhance prediction performance. When applied to the datasets from AntiJen database and IEDB database, our method produces better performances than several popular quantitative methods. 相似文献
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为了提高网络流量的预测精度,提出一种布谷鸟算法优化混合核相关向量机的网络流量预测模型(CS-RVM)。首先采用多项式和高斯核函数构成混合核函数代替相关向量机的单一核函数,然后引入布谷鸟算法对混合核参数进行寻优,最后建立网络流量预测模型。仿真结果表明,CS-RVM具有良好的建模效果,可提高网络流量的预测精度。 相似文献
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为了提高网络安全态势的预测精度,针对单一核函数的局限性,提出一种组合核函数相关向量机的网络安全态势预测模型。首先对网络安全态势时间序列进行重新构造,得到相关向量机的学习样本,然后采用多项式和高斯核函数构建组合核函数,并采用组合核函数相关向量机对网络安全态势样本进行学习,建立网络安全态势预测模型,最后对网络安全态势预测性能进行测试。实验结果表明,相对于单一核函数相关向量机以及其它网络安全态势预测模型,组合核函数相关向量机提高了网络安全态势的预测准确性,可以满足网络安全态势预测的实际应用需求 相似文献
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Peptide vaccination for cancer immunotherapy requires identification of peptide epitopes derived from antigenic proteins associated with the tumor. Such peptides can bind to MHC proteins (MHC molecules) on the tumor-cell surface, with the potential to initiate a host immune response against the tumor. Computer prediction of peptide epitopes can be based on known motifs for peptide sequences that bind to a certain MHC molecule, on algorithms using experimental data as a training set, or on structure-based approaches. We have developed an algorithm, which we refer to as PePSSI, for flexible structural prediction of peptide binding to MHC molecules. Here, we have applied this algorithm to identify peptide epitopes (of nine amino acids, the common length) from the sequence of the cancer-testis antigen KU-CT-1, based on the potential of these peptides to bind to the human MHC molecule HLA-A2. We compared the PePSSI predictions with those of other algorithms and found that several peptides predicted to be strong HLA-A2 binders by PePSSI were similarly predicted by another structure-based algorithm, PREDEP. The results show how structure-based prediction can identify potential peptide epitopes without known binding motifs and suggest that side chain orientation in binding peptides may be obtained using PePSSI. 相似文献
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T细胞表位预测技术对于减少实验合成重叠肽、研究病原体与机体作用的免疫机制以及深入理解T细胞介导的免疫特异性均有重要意义。为增强T细胞表位预测模型的可理解性,本文在通过肽的预处理构建出存储等长肽段的决策表之后,设计出了一种基于粗集的T细胞表位预测方法。该方法由基于信息熵的属性约简完备算法和基于锚点知识的属性值顺序约简改进算法共同组成。基于HLA-DR4(B10401)编码的MHCII类分子结合肽的实验数据表明,在预测精度与传统神经网络方法大致相当的基础上,本文方法可以提取出用于帮助专家理解MHC分子与抗原肽结合机理的产生式规则。 相似文献
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用于T细胞表位预测的分类器集成方法* 总被引:1,自引:1,他引:0
T细胞表位预测技术对于减少实验合成重叠肽,理解T细胞介导的免疫特异性和研制亚单位多肽及基因疫苗均有重要意义.为弥补已有基于机器学习方法的T细胞表位预测模型的可理解性的不足并进一步提高模型的预测精度,首先通过肽的预处理构建出了存储等长肽段的决策表,而后提出了基于粗糙集的分类器集成算法.该算法不但综合利用了基于信息熵的属性约简完备算法和其他属性约简算法的优势,而且将T细胞表位预测领域中的锚点知识融入到了属性值约简过程中.最后利用该算法来预测MHC Ⅱ类分子HLA-DR4(B1·0401)的结合肽,首次提取出了预测精度高且能帮助专家理解MHC分子与抗原肽的结合机理的产生式规则,为下一步的分子建模工作奠定了基础. 相似文献
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A set of high quality structures of protein-ligand complexes with experimentally determined binding affinities has been extracted from the Protein Data Bank and used to test and recalibrate AutoDock force field. Since for some binding sites water molecules are crucial for bridging the receptor-ligand interactions, they have to be included in the analysis. To simplify the process of incorporating water molecules into the binding sites and make it less ambiguous, new simple water model was created. After recalibration of the force field on the new dataset much better correlation between the computed and experimentally determined binding affinities was achieved and the quality of pose prediction improved even more. 相似文献
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针对小时间尺度网络流量预测中的复杂性、非线性和高度自相似性等问题,创新性的提出了一种改进的模拟退火法优化相关向量机的网络流量预测模型(PSA-RVM)。将网络流量时间序列进行相空间重构,同时采用改进模拟退火法优化相关向量机的超参数,进而构建网络流量PSA-RVM预测模型。试验表明,PSA-RVM预测模型的预测精度、稳定性都优于RVM模型和PSO-SVR模型。 相似文献
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Costantini S Rossi M Colonna G Facchiano AM 《Journal of molecular graphics & modelling》2005,23(5):419-431
Celiac disease (CD) is sustained by abnormal intestinal mucosal T-cell response to gluten and it is strongly associated with HLA class II molecules encoded by DQA1*0501/DQB1*02 (DQ2) or DQA1*03/DQB1*0302 (DQ8). The in vitro stimulatory activity of gliadin increases after treatment with tissue transglutaminase (tTG) which catalyses the deamidation of specific residues of glutamine to glutamate that can serve as anchors for binding to DQ2 as well as to DQ8 molecules. We modelled the three-dimensional structure of the DQ2 dimer protein, the most frequent in celiac patients, by using a homology modelling strategy, and deposited the model in the Protein Data Bank (PDB). Then, we simulated the interactions of DQ2 with different gluten peptides and the deamidation of specific peptide glutamines in the known p4, p6, p7 and p9 anchor positions, as well as in p1 and p5 positions, and other substitutions for which experimental effects on binding are available by previous experimental studies. By evaluating the energy of interaction and the H-bond interactions, we were able to distinguish what substitutions improve the interaction peptide-DQ2, in agreement with previously published experimental data. By analysing the peptide-DQ2 complex at the atom level, we observed that these glutamate side chains can interact with specific positively charged amino acids of DQ2, absent in other HLA alleles not related to celiac disease. The simulation was also extended to other peptides, related to celiac disease but for which no experimental data exists about the effects of glutamine deamidation. Our results give an interpretation at the molecular level of previously reported binding experimental data and open a new window to gain further insights about peptide recognition in celiac disease. 相似文献
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This study concerns with the control of basic oxygen furnace (BOF) steelmaking process and proposes a dynamic control model based on adaptive-network-based fuzzy inference system (ANFIS) and robust relevance vector machine (RRVM). The model aims to control the second blow period of BOF steelmaking and consists of two parts, the first of which is to calculate the values of control variables, viz., the amounts of oxygen and coolant requirement, and the other is to predict the endpoint carbon content and temperature of molten steel. In the first part, an ANFIS classifier is primarily constructed to determine whether coolant should be added or not, then an ANFIS regression model is utilized to calculate the amounts of oxygen and coolant. In the second part, a novel robust relevance vector machine is presented to predict the endpoint. RRVM solves the problem of sensitivity to outlier characteristic of classical relevance vector machine, thus obtaining higher prediction accuracy. The key idea of the proposed RRVM is to introduce individual noise variance coefficient to each training sample. In the process of training, the noise variance coefficients of outliers gradually decrease so as to reduce the impact of outliers and improve the robustness of the model. Simulations on industrial data show that the proposed dynamic control model yields good results on the oxygen and coolant calculation as well as endpoint prediction. It is promising to be utilized in practical BOF steelmaking process. 相似文献
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Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically. 相似文献
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针对相关向量机的性能易受到奇异值影响的情况,提出了一种增强相关向量机稳健性的方法。其主要思想如下:首先用原始训练数据训练相关向量机;然后,利用某种准则,从原始数据中挑选一些样本,用其预测值代替输出变量值;随后,用改变后的训练样本重新训练相关向量机。这个过程可重复几次。数据试验表明,较之相关向量机和变分稳健相关向量机,新算法对奇异值更加不敏感。 相似文献
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Quantitative online prediction of peptide binding to the major histocompatibility complex 总被引:3,自引:0,他引:3
Hattotuwagama CK Guan P Doytchinova IA Zygouri C Flower DR 《Journal of molecular graphics & modelling》2004,22(3):195-207
With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501. 相似文献
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HIV-1 protease has been the subject of intense research for deciphering HIV-1 virus replication process for decades. Knowledge of the substrate specificity of HIV-1 protease will enlighten the way of development of HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, various feature encoding techniques and machine learning algorithms have been used frequently. In this paper, a new feature amino acid encoding scheme is proposed to predict HIV-1 protease cleavage sites. In the proposed method, we combined orthonormal encoding and Taylor’s venn-diagram. We used linear support vector machines as the classifier in the tests. We also analyzed our technique by comparing some feature encoding techniques. The tests are carried out on PR-1625 and PR-3261 datasets. Experimental results show that our amino acid encoding technique leads to better classification performance than other encoding techniques on a standalone classifier. 相似文献