共查询到20条相似文献,搜索用时 15 毫秒
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Gabriel Bianchin de Oliveira Helio Pedrini Zanoni Dias 《International journal of molecular sciences》2021,22(21)
Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem—driven by the recent results obtained by computational methods in this task—(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers—six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually. 相似文献
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Yongbeom Kwon Woong-Hee Shin Junsu Ko Juyong Lee 《International journal of molecular sciences》2020,21(22)
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important. 相似文献
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Structure and electronic properties of two unusual boron clusters obtained by fusion of borozene rings have been studied by means of first principles calculations based on the generalized-gradient approximation of the density functional theory. Moreover, a semiempirical tight-binding model has been appropriately calibrated for transport calculations on these clusters. Results show that the pure boron clusters are topologically planar and characterized by (3c–2e) bonds, which can explain, together with the aromaticity (estimated by means of NICS), the remarkable cohesive energy values obtained. Such feature makes these systems competitive with the most stable boron clusters to date. The energy gap values indicate that these clusters possess a semiconducting character, while when the larger system is considered, zero-values of the density of states are found exclusively within the HOMO–LUMO gap. Electron transport calculations within the Landauer formalism confirm these indications, showing semiconductor-like low bias differential conductance for these structures. Differences and similarities with carbon clusters are highlighted in the discussion. 相似文献
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Hongwei Wu & Stavros Avramidis 《Drying Technology》2013,31(12):1541-1545
The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method. 相似文献
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In order to catch more process details in chemical processes,a dynamic model for prediction of process trends is proposed by modifying traditional time-series ANN (artificial neural networks) model with impules response indentification means.The application result of the model is briefly discussed. 相似文献
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ANN方法分析预测聚丙烯材料的力学性能 总被引:1,自引:0,他引:1
利用B-P人工神经网络(AJNN)对聚丙烯(PP)的力学性能进行了分析和预测。首先将PP材料接纯PP、共混和增韧及填充和增强PP等进行分类,并根据这些数据的特点建立B-P网络,然后用各类PP材料的组成和力学性能数据对网络进行学习训练,最后用“未知样品”的数据对网络进行验证。结果表明,所建立的网络能反映PP的力学性能特性,预测有一定的准确性,但不同类别的材料预测准确性不同。 相似文献
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基于典型相关分析和数据自回归处理的BP神经网络在聚丙烯熔融指数预报中的应用 总被引:2,自引:2,他引:0
由于聚丙烯生产是一个大量参数相互耦合的强非线性过程,使得传统的机理建模受到一定的限制。提出基于典型相关分析和数据自回归处理的BP神经网络软测量建模,通过可测变量来推知聚丙烯熔融指数。应用典型相关分析选择与输出熔融指数关系较大的独立输入变量,数据自回归处理校正一系列带有误差的量测数据,而BP神经网络用来刻画过程的非线性特征。最后,将提出的算法应用到聚丙烯大型生产工艺中进行熔融指数的预报建模并进行实例仿真,仿真结果表明该算法有较强的建模精度。 相似文献
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Jiazhi Song Guixia Liu Jingqing Jiang Ping Zhang Yanchun Liang 《International journal of molecular sciences》2021,22(2)
Accurately identifying protein–ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein–ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods. 相似文献
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基于GA-BP网络混凝投药系统预测模型的研究 总被引:1,自引:1,他引:0
针对BP网络建模易陷入局部极小、收敛速度慢等缺点,建立GA-BP网络预测模型,为混凝投药系统生产指导提供决策依据。利用遗传学习算法具有全局寻优的特点,同时优化BP网络的初始权值和网络结构,建立GA-BPNN混凝投药的预测控制模型。通过算法比较和模型仿真结果分析,GA-BP混合模型较BP模型收敛速度快,其平均预测相对误差仅为9.94%,预测精度远高于BP模型。表明GA-BP模型可以有效、可靠地用于混凝剂投加量预测控制系统的生产指导中。 相似文献
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Anna Helena Mazurek ukasz Szeleszczuk Dariusz Maciej Pisklak 《International journal of molecular sciences》2021,22(9)
This review focuses on a combination of ab initio molecular dynamics (aiMD) and NMR parameters calculations using quantum mechanical methods. The advantages of such an approach in comparison to the commonly applied computations for the structures optimized at 0 K are presented. This article was designed as a convenient overview of the applied parameters such as the aiMD type, DFT functional, time step, or total simulation time, as well as examples of previously studied systems. From the analysis of the published works describing the applications of such combinations, it was concluded that including fast, small-amplitude motions through aiMD has a noticeable effect on the accuracy of NMR parameters calculations. 相似文献
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The main objective of this study was to develop simple models for the prediction of bromate formation in ozonated bottled waters, using rapidly and practically measurable raw water quality and/or operational parameters. A total of 6 multi-linear regression (MLR) with or without principal component analysis (PCA) and 2 artificial neural networks (ANN) models with multilayer perceptron architecture were developed for the prediction of bromate formation. PCA was employed to better identify relations between variables and reduce the number of variables. Experimental data used in modeling was provided from the ozonation of samples from 5 groundwater sources at various applied ozone dose and contact time. MLR models#1 and #2 well-predicted bromate formation although correlations (i.e., the signs of regression constants) among pH (as input variable) and bromate concentrations did not agree with the chemistry. MLR model#6, containing practical input parameters that are measured on-line in full-scale treatment plants, adequately predicted bromate formation and agreed with the chemistry, although fewer input parameters were used compared to MLR#1 and #2. Although both of the ANN models exhibited high regression coefficients (R2) (0.97 for both) ANN#1 was found to provide better prediction of bromate formation based on mean square error (MSE) values. However, since ANN#2 included easily measurable input parameters it may be practically used by water companies employing ozonation. Results overall indicated that ANN models have stronger prediction capabilities of bromate formation than MLR models. ANN modeling appears to be a strong tool in situations where the relations between variables are non-linear, interactive and complex, as in the bromate formation by ozonation. 相似文献
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Zheng-Wei Li Zhu-Hong You Xing Chen Jie Gui Ru Nie 《International journal of molecular sciences》2016,17(9)
Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research. 相似文献
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An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous twodimensional model is used to generate simulation data to investigate the prediction scheme presentedunder randomly changing operating conditions.The scheme consisting of the K-L expansion andneural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures. 相似文献
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Time-series prediction is one of themajor methodologies used for fault prediction. Themethods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problemof reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the outputweight of the reservoir neural network. As a result, the amplitude of outputweight is effectively controlled and the ill-posedness problemis solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey-Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data. The final prediction correct rate reaches 81%. 相似文献
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根据HPFL加固层和加固混凝土构件之间的243个正拉粘结强度试验测试和24个剪切粘结强度试验测试,将影响二者粘结强度的主要因素,如抹灰龄期、加固界面粗糙度、混凝土和砂浆强度、修补方位等作为特征参数,建立了预测HPFL加固层与混凝土粘结强度的BP人工神经网络模型.采用训练好的BP神经网络对HPFL加固层与混凝土粘结强度进行了预测,并与实测值进行了对比.正拉粘结强度预测值与试验值之比的平均值为1.056,标准差为0.057;剪切粘结强度预测值与试验值之比的平均值为0.988,标准差为0.127.结果表明:预测值与试验值符合良好,利用BP神经网络对HPFL加固层与混凝土粘结强度进行预测是可行的. 相似文献