Hierarchical genetic algorithm for near optimal feedforward neural network design

In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi layer Perceptron networks and radial basis function networks. Based upon the chosen cost function, a linear weight combination decision making approach has been applied to derive an approximated Pareto optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two objective optimization problem.     

Characterizing proteolytic cleavage site activity using bio-basis function neural networks

MOTIVATION: In protein chemistry, proteomics and biopharmaceutical development, there is a desire to know not only where a protein is cleaved by a protease, but also the susceptibility of its cleavage sites. The current tools for proteolytic cleavage prediction have often relied purely on regular expressions, or involve models that do not represent biological data well. RESULTS: A novel methodology for characterizing proteolytic cleavage site activities has been developed, which incorporates two fundamental features: activity class prediction and the use of an amino acid similarity matrix for (non-parametric) neural learning. The first solved the problem of predicting proteolytic efficiency. The second significantly improved the robustness in prediction and reduced the time complexity for learning. This study shows that activity class prediction is successful when applying this methodology to the prediction and characterization of Trypsin cleavage sites and the prediction of HIV protease cleavage sites. AVAILABILITY: Requests for software and data should be made respectively to Dr Zheng Rong Yang and Miss Rebecca Thomson.     

Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks

MOTIVATION: Apoptosis has drawn the attention of researchers because of its importance in treating some diseases through finding a proper way to block or slow down the apoptosis process. Having understood that caspase cleavage is the key to apoptosis, we find novel methods or algorithms are essential for studying the specificity of caspase cleavage activity and this helps the effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, there is a motivation, in this study, to investigate the application of bio-basis function neural networks for the prediction of caspase cleavage sites. RESULTS: Thirteen protein sequences with experimentally determined caspase cleavage sites were downloaded from NCBI. Bayesian bio-basis function neural networks are investigated and the comparisons with single-layer perceptrons, multilayer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size used to generate sub-sequences for modelling on prediction accuracy is studied. The results show that the Bayesian bio-basis function neural network with two Gaussian distributions for model parameters (weights) performed the best and the highest prediction accuracy is 97.15 +/- 1.13%. AVAILABILITY: The package of Bayesian bio-basis function neural network can be obtained by request to the author.     

Predicting human immunodeficiency virus protease cleavage sites in nonlinear projection space

HIV-1 protease has a broad and complex substrate specificity. The discovery of an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease would greatly expedite the search for inhibitors of HIV protease. During the last two decades, various methods have been developed to explore the specificity of HIV protease cleavage activity. However, because little advancement has been made in the understanding of HIV-1 protease cleavage site specificity, not much progress has been reported in either extracting effective methods or maintaining high prediction accuracy. In this article, a theoretical framework is developed, based on the kernel method for dimensionality reduction and prediction for HIV-1 protease cleavage site specificity. A nonlinear dimensionality reduction kernel method, based on manifold learning, is proposed to reduce the high dimensions of protease specificity. A support vector machine is applied to predict the protease cleavage. Superior performance in comparison to that previously published in literature is obtained using numerical simulations showing that the basic specificities of the HIV-1 protease are maintained in reduction feature space, and by combining the nonlinear dimensionality reduction algorithm with a support vector machine classifier.     

Mining HIV protease cleavage data using genetic programming with a sum-product function

MOTIVATION: In order to design effective HIV inhibitors, studying and understanding the mechanism of HIV protease cleavage specification is critical. Various methods have been developed to explore the specificity of HIV protease cleavage activity. However, success in both extracting discriminant rules and maintaining high prediction accuracy is still challenging. The earlier study had employed genetic programming with a min-max scoring function to extract discriminant rules with success. However, the decision will finally be degenerated to one residue making further improvement of the prediction accuracy difficult. The challenge of revising the min-max scoring function so as to improve the prediction accuracy motivated this study. RESULTS: This paper has designed a new scoring function called a sum-product function for extracting HIV protease cleavage discriminant rules using genetic programming methods. The experiments show that the new scoring function is superior to the min-max scoring function. AVAILABILITY: The software package can be obtained by request to Dr Zheng Rong Yang.     

Short-term prediction of chaotic time series by using RBF network with regression weights

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.     

基于径向基函数神经网络的心电图ST段形态识别

心电图的ST段是指QRS波的终点至T波的起点间的一个子波,其时间长度与心率有关,对ST段形态的识别有助于分析ST段变化的原因和确定缺血的部位。将模糊逻辑系统与神经网络相结合,利用基于自适应模糊系统的径向基函数神经网络对心电信号ST段的形态识别进行了研究。该网络比BP网络学习进度快,具有增量学习的能力,它能够识别学习外的新模式。研究取得了较好的识别结果。     

Ozone prediction on the basis of neural networks,support vector regression and methods with uncertainty

The article presents modeling of daily average ozone level prediction by means of neural networks, support vector regression and methods based on uncertainty. Based on data measured by a monitoring station of the Pardubice micro-region, the Czech Republic, and optimization of the number of parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, ε-support vector regression, fuzzy inference systems and Takagi–Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Takagi–Sugeno intuitionistic fuzzy inference system and adaptation of fuzzy logic-based systems using evolutionary algorithms. Based on data obtained, the daily average ozone level prediction in a certain time is characterized by a root mean squared error. The best possible results were obtained by means of an ε-support vector regression with polynomial kernel functions and Takagi–Sugeno intuitionistic fuzzy inference systems with adaptation by means of a Kalman filter.     

基于 RBF 神经网络的蛋白质二级结构预测

蛋白质二级结构对于研究其功能具有重要作用。采用主成分分析方法对氨基酸的基本物化属性及其二级结构倾向性进行降维降噪处理,使用径向基神经网络对蛋白质二级结构进行预测。主成分分析使得之前 20 ×12 矩阵变为 20 ×4 矩阵,极大地减少了神经网络输入端的维数。在仿真过程中,当窗口大小为 21,扩展函数为 7 时,预测精确度达到了 71. 81%。实验结果表明 RBF 神经网络可以有效的用于蛋白质二级结构的预测。     

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.     

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.     

Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm

Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.     

Genome-enabled prediction of genetic values using radial basis function neural networks

The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait-environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.     

Mining viral protease data to extract cleavage knowledge

MOTIVATION: The motivation is to identify, through machine learning techniques, specific patterns in HIV and HCV viral polyprotein amino acid residues where viral protease cleaves the polyprotein as it leaves the ribosome. An understanding of viral protease specificity may help the development of future anti-viral drugs involving protease inhibitors by identifying specific features of protease activity for further experimental investigation. While viral sequence information is growing at a fast rate, there is still comparatively little understanding of how viral polyproteins are cut into their functional unit lengths. The aim of the work reported here is to investigate whether it is possible to generalise from known cleavage sites to unknown cleavage sites for two specific viruses-HIV and HCV. An understanding of proteolytic activity for specific viruses will contribute to our understanding of viral protease function in general, thereby leading to a greater understanding of protease families and their substrate characteristics. RESULTS: Our results show that artificial neural networks and symbolic learning techniques (See5) capture some fundamental and new substrate attributes, but neural networks outperform their symbolic counterpart.     

Enteroviral protease 2A directly cleaves dystrophin and is inhibited by a dystrophin-based substrate analogue

Enteroviruses such as Coxsackievirus B3 can cause dilated cardiomyopathy through unknown pathological mechanism(s). Dystrophin is a large extrasarcomeric cytoskeletal protein whose genetic deficiency causes hereditary dilated cardiomyopathy. In addition, we have recently shown that dystrophin is proteolytically cleaved by the Coxsackievirus protease 2A leading to functional impairment and morphological disruption. However, the mechanism of dystrophin cleavage and the exact cleavage site remained to be identified. Antibody epitope mapping of endogenous dystrophin indicated protease 2A-mediated cleavage at the site in the hinge 3 region predicted by a neural network algorithm (human, amino acid 2434; mouse, amino acid 2427). Using site-directed mutagenesis, peptide sequencing, and fluorescence resonance energy transfer assays with recombinant dystrophin, we demonstrate that this putative site in mouse and human dystrophin is a direct substrate for the Coxsackieviral protease 2A both in vitro and in vivo. The substrate analogue protease inhibitor z-LSTT-fmk was designed based on the dystrophin sequence that interacts with the protease 2A and was found to have an IC(50) of 550 nM in vitro. Dystrophin is the first cellular substrate of the enteroviral protease 2A that was identified using by a bioinformatic approach and for which the cleavage site was molecularly mapped within living cells.     

A new sequence representation as applied in better specificity elucidation for human immunodeficiency virus type 1 protease

Factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility, and electronic properties were derived from 516 property parameters of 20-coded amino acids, and was then employed to represent sequence structures of 746 peptides with 8 amino acid residues. Cleavage site prediction models for human immunodeficiency virus type 1 protease by linear discriminant analysis and support vector machine with radial basis function kernel were constructed to identify if they could be cleaved or not, and were further utilized to investigate the cleavage specificity. These diversified properties, including the bulky properties, secondary conformation characteristics, electronic properties, and hydrophobicity at the first, the second, the fourth, the fifth, and the sixth residue, are possibly important factors in determining HIV PR cleavage or not. Particularly, maximal positive and negative influences result from the bulky properties of different sites. Further results from analysis of variance also likely reflect that the HIV PR recognizes diversified key properties of various sites in the octameric sequences. Satisfactory results show that FASGAI can not only be used to represent sequence structures of various functional peptides, but alsoprovide a potential feasible measure for exploring relationship between protein motif sequences and their functions.     

A novel efficient learning algorithm for self-generating fuzzy neural network with applications

In this paper, a novel efficient learning algorithm towards self-generating fuzzy neural network (SGFNN) is proposed based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy system. The proposed algorithm is simple and efficient and is able to generate a fuzzy neural network with high accuracy and compact structure. The structure learning algorithm of the proposed SGFNN combines criteria of fuzzy-rule generation with a pruning technology. The Kalman filter (KF) algorithm is used to adjust the consequent parameters of the SGFNN. The SGFNN is employed in a wide range of applications ranging from function approximation and nonlinear system identification to chaotic time-series prediction problem and real-world fuel consumption prediction problem. Simulation results and comparative studies with other algorithms demonstrate that a more compact architecture with high performance can be obtained by the proposed algorithm. In particular, this paper presents an adaptive modeling and control scheme for drug delivery system based on the proposed SGFNN. Simulation study demonstrates the ability of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.     

Variable context Markov chains for HIV protease cleavage site prediction

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.