Biomedical subjects

Zheng Rong Yang

Publications and source records attributed to Zheng Rong Yang.

At least 19 recordsLinked to original sources

PubMed2006

A bio-basis function neural network for protein peptide cleavage activity characterisation.

This paper presents a novel neural learning algorithm for analysing protein peptides which comprise amino acids as non-numerical attributes. The algorithm is derived from the radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used by RBFNNs with a bio-basis function. Each basis in BBFNN is supported by a peptide. The bases collectively form a feature space, in which each basis represents a feature dimension. A linear classifier is constructed in the feature space for characterising a protein peptide in terms of functional status. The theoretical basis of BBFNN is that peptides, which perform the same function will have similar compositions of amino acids. Because of this, the similarity between peptides can have statistical significance for modelling while the proposed bio-basis function can well code this information from data. The application to two real cases shows that BBFNN outperformed multi-layer perceptrons and support vector machines.

Algorithms↗

PubMed2006

Predicting hepatitis C virus protease cleavage sites using generalized linear indicator regression models.

This paper discusses how to predict hepatitis C virus protease cleavage sites in proteins using generalized linear indicator regression models. The mutual information is used for model-size optimization. Two simulation strategies are adopted, i.e., building a model based on published peptides and building a model based on the published peptides plus newly collected sequences. It is found that the latter outperforms the former significantly. The simulation also shows that the generalized linear indicator regression model far outperforms the multilayer perceptron model.

Algorithms↗

PubMed2006

A novel radial basis function neural network for discriminant analysis.

A novel radial basis function neural network for discriminant analysis is presented in this paper. In contrast to many other researches, this work focuses on the exploitation of the weight structure of radial basis function neural networks using the Bayesian method. It is expected that the performance of a radial basis function neural network with a well-explored weight structure can be improved. As the weight structure of a radial basis function neural network is commonly unknown, the Bayesian method is, therefore, used in this paper to study this a priori structure. Two weight structures are investigated in this study, i.e., a single-Gaussian structure and a two-Gaussian structure. An expectation-maximization learning algorithm is used to estimate the weights. The simulation results showed that the proposed radial basis function neural network with a weight structure of two Gaussians outperformed the other algorithms.

Algorithms↗

PubMed2006

Prediction of signal peptides using bio-basis function neural networks and decision trees.

Signal peptide identification is of immense importance in drug design. Accurate identification of signal peptides is the first critical step to be able to change the direction of the targeting proteins and use the designed drug to target a specific organelle to correct a defect. Because experimental identification is the most accurate method, but is expensive and time-consuming, an efficient and affordable automated system is of great interest. In this article, we propose using an adapted neural network, called a bio-basis function neural network, and decision trees for predicting signal peptides. The bio-basis function neural network model and decision trees achieved 97.16% and 97.63% accuracy respectively, demonstrating that the methods work well for the prediction of signal peptides. Moreover, decision trees revealed that position P(1'), which is important in forming signal peptides, most commonly comprises either leucine or alanine. This concurs with the (P(3)-P(1)-P(1')) coupling model.

Algorithms↗

PubMed2005

RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins.

MOTIVATION: Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability. RESULTS: We have developed the regional order neural network (RONN) software as an application of our recently developed 'bio-basis function neural network' pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the Protein Data Bank shows that, based on the probability excess measure, RONN performed the best.

Algorithms↗

PubMed2005

Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.

MOTIVATION: Although the outbreak of the severe acute respiratory syndrome (SARS) is currently over, it is expected that it will return to attack human beings. A critical challenge to scientists from various disciplines worldwide is to study the specificity of cleavage activity of SARS-related coronavirus (SARS-CoV) and use the knowledge obtained from the study for effective inhibitor design to fight the disease. The most commonly used inductive programming methods for knowledge discovery from data assume that the elements of input patterns are orthogonal to each other. Suppose a sub-sequence is denoted as P2-P1-P1'-P2', the conventional inductive programming method may result in a rule like 'if P1 = Q, then the sub-sequence is cleaved, otherwise non-cleaved'. If the site P1 is not orthogonal to the others (for instance, P2, P1' and P2'), the prediction power of these kind of rules may be limited. Therefore this study is aimed at developing a novel method for constructing non-orthogonal decision trees for mining protease data. RESULT: Eighteen sequences of coronavirus polyprotein were downloaded from NCBI (http://www.ncbi.nlm.nih.gov). Among these sequences, 252 cleavage sites were experimentally determined. These sequences were scanned using a sliding window with size k to generate about 50,000 k-mer sub-sequences (for short, k-mers). The value of k varies from 4 to 12 with a gap of two. The bio-basis function proposed by Thomson et al. is used to transform the k-mers to a high-dimensional numerical space on which an inductive programming method is applied for the purpose of deriving a decision tree for decision-making. The process of this transform is referred to as a bio-mapping. The constructed decision trees select about 10 out of 50,000 k-mers. This small set of selected k-mers is regarded as a set of decisive templates. By doing so, non-orthogonal decision trees are constructed using the selected templates and the prediction accuracy is significantly improved.

Algorithms↗

PubMed2005

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.

Algorithms↗

PubMed2005

Bio-basis function neural network for prediction of protease cleavage sites in proteins.

The prediction of protease cleavage sites in proteins is critical to effective drug design. One of the important issues in constructing an accurate and efficient predictor is how to present nonnumerical amino acids to a model effectively. As this issue has not yet been paid full attention and is closely related to model efficiency and accuracy, we present a novel neural learning algorithm aimed at improving the prediction accuracy and reducing the time involved in training. The algorithm is developed based on the conventional radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used in RBFNNs by a novel bio-basis function. Each bio-basis is a feature dimension in a numerical feature space, to which a nonnumerical sequence space is mapped for analysis. The bio-basis function is designed using an amino acid mutation matrix verified in biology. Thus, the biological content in protein sequences can be maximally utilized for accurate modeling. Mutual information (MI) is used to select the most informative bio-bases and an ensemble method is used to enhance a decision-making process, hence, improving the prediction accuracy further. The algorithm has been successfully verified in two case studies, namely the prediction of Human Immunodeficiency Virus (HIV) protease cleavage sites and trypsin cleavage sites in proteins.

Artificial Intelligence↗

PubMed2005

Orthogonal kernel machine for the prediction of functional sites in proteins.

A novel pattern recognition algorithm called an orthogonal kernel machine (OKM) is presented for the prediction of functional sites in proteins. Two novelties in OKM are that the kernel function is specially designed for measuring the similarity between a pair of protein sequences and the kernels are selected using the orthogonal method. Based on a set of well-recognized orthogonal kernels, this algorithm demonstrates its superior performance compared with other methods. An application of this algorithm to a real problem is presented.

Algorithms↗

PubMed2005

Pattern recognition methods for protein functional site prediction.

Protein functional site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the functional sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein functional site prediction and outline the future research directions in this important area.

Algorithms↗

PubMed2004

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.

Algorithms↗

PubMed2004

Mining gene expression data based on template theory.

MOTIVATION: It is understood that clustering genes are useful for exploring scientific knowledge from DNA microarray gene expression data. The explored knowledge can be finally used for annotating biological function for novel genes. Representing the explored knowledge in an efficient manner is then closely related to the classification accuracy. However, this issue has not yet been paid the attention it deserves. RESULT: A novel method based on template theory in cognitive psychology and pattern recognition is developed in this study for representing knowledge extracted from cluster analysis effectively. The basic principle is to represent knowledge according to the relationship between genes and a found cluster structure. Based on this novel knowledge representation method, a pattern recognition algorithm (the decision tree algorithm C4.5) is then used to construct a classifier for annotating biological functions of novel genes. The experiments on five published datasets show that this method has improved the classification performance compared with the conventional method. The statistical tests indicate that this improvement is significant. AVAILABILITY: The software package can be obtained upon request from the author.

Algorithms↗

PubMed2004

Bio-support vector machines for computational proteomics.

MOTIVATION: One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias. RESULTS: By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.

Algorithms↗

PubMed2004

Predicting the linkage sites in glycoproteins using bio-basis function neural network.

MOTIVATION: Although, it is known that O-glycosidically linked oligosaccharides are commonly conjugated to a serine, threonine or hydroxylysine residue of the polypeptide, the chemical nature of the anchoring monosaccharide and the size of the oligosaccharide unit varies. Among different types, O-linked or mucin-type oligosaccharides are intimately involved in the secretion of proteins, be they enzymes, hormones or structural glycoproteins. Knowledge of the linkage sites in glycoproteins is critical to the design of specific and efficient inhibitors against the enzyme to catalyse the formation of the carbohydrate-peptide linkage. RESULTS: We present a method for predicting the linkage sites in O-linked glycoproteins using bio-basis function neural networks. The mean prediction accuracy of this method is 91.15 +/- 2.75% while it is 82.28 +/- 6.45% using back-propagation neural networks. Importantly, this method has significantly reduced the CPU time for modelling.

Algorithms↗

PubMed2004

Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms.

Protein phosphorylation is a post-translational modification performed by a group of enzymes known as the protein kinases or phosphotransferases (Enzyme Commission classification 2.7). It is essential to the correct functioning of both proteins and cells, being involved with enzyme control, cell signalling and apoptosis. The major problem when attempting prediction of these sites is the broad substrate specificity of the enzymes. This study employs back-propagation neural networks (BPNNs), the decision tree algorithm C4.5 and the reduced bio-basis function neural network (rBBFNN) to predict phosphorylation sites. The aim is to compare prediction efficiency of the three algorithms for this problem, and examine knowledge extraction capability. All three algorithms are effective for phosphorylation site prediction. Results indicate that rBBFNN is the fastest and most sensitive of the algorithms. BPNN has the highest area under the ROC curve and is therefore the most robust, and C4.5 has the highest prediction accuracy. C4.5 also reveals the amino acid 2 residues upstream from the phosporylation site is important for serine/threonine phosphorylation, whilst the amino acid 3 residues upstream is important for tyrosine phosphorylation.

Algorithms↗

PubMed2004

Biological applications of support vector machines.

One of the major tasks in bioinformatics is the classification and prediction of biological data. With the rapid increase in size of the biological databanks, it is essential to use computer programs to automate the classification process. At present, the computer programs that give the best prediction performance are support vector machines (SVMs). This is because SVMs are designed to maximise the margin to separate two classes so that the trained model generalises well on unseen data. Most other computer programs implement a classifier through the minimisation of error occurred in training, which leads to poorer generalisation. Because of this, SVMs have been widely applied to many areas of bioinformatics including protein function prediction, protease functional site recognition, transcription initiation site prediction and gene expression data classification. This paper will discuss the principles of SVMs and the applications of SVMs to the analysis of biological data, mainly protein and DNA sequences.

Algorithms↗

PubMed2004

Reduced bio-basis function neural networks for protease cleavage site prediction.

This paper presents a new neural learning algorithm for protease cleavage site prediction. The basic idea is to replace the radial basis function used in radial basis function neural networks by a so-called bio-basis function using amino acid similarity matrices. Mutual information is used to select bio-bases and a corresponding selection algorithm is developed. The algorithm has been applied to the prediction of HIV and Hepatitis C virus protease cleavage sites in proteins with success.

Algorithms↗

PubMed2003

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.

Amino Acid Sequence↗