Boosting classifier for predicting protein domain structural class

A novel classifier, the so-called “LogitBoost” classifier, was introduced to predict the structural class of a protein domain according to its amino acid sequence. LogitBoost is featured by introducing a log-likelihood loss function to reduce the sensitivity to noise and outliers, as well as by performing classification via combining many weak classifiers together to build up a very strong and robust classifier. It was demonstrated thru jackknife cross-validation tests that LogitBoost outperformed other classifiers including “support vector machine,” a very powerful classifier widely used in biological literatures. It is anticipated that LogitBoost can also become a useful vehicle in classifying other attributes of proteins according to their sequences, such as subcellular localization and enzyme family class, among many others.     

Support Vector Machines for Predicting Apoptosis Proteins Types

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death, and their function is related to their types. According to the classification scheme by Zhou and Doctor (2003), the apoptosis proteins are categorized into the following four types: (1) cytoplasmic protein; (2) plasma membrane-bound protein; (3) mitochondrial inner and outer proteins; (4) other proteins. A powerful learning machine, the Support Vector Machine, is applied for predicting the type of a given apoptosis protein by incorporating the sqrt-amino acid composition effect. High success rates were obtained by the re-substitute test (98/98 = 100 %) and the jackknife test (89/98 = 90.8%).     

Support Vector Machines for Protein Fold Class Prediction

Knowledge of the three‐dimensional structure of a protein is essential for describing and understanding its function. Today, a large number of known protein sequences faces a small number of identified structures. Thus, the need arises to predict structure from sequence without using time‐consuming experimental identification. In this paper the performance of Support Vector Machines (SVMs) is compared to Neural Networks and to standard statistical classification methods as Discriminant Analysis and Nearest Neighbor Classification. We show that SVMs can beat the competing methods on a dataset of 268 protein sequences to be classified into a set of 42 fold classes. We discuss misclassification with respect to biological function and similarity. In a second step we examine the performance of SVMs if the embedding is varied from frequencies of single amino acids to frequencies of tripletts of amino acids. This work shows that SVMs provide a promising alternative to standard statistical classification and prediction methods in functional genomics.     

Support vector machines for prediction of protein domain structural class

The support vector machines (SVMs) method was introduced for predicting the structural class of protein domains. The results obtained through the self-consistency test, jack-knife test, and independent dataset test have indicated that the current method and the elegant component-coupled algorithm developed by Chou and co-workers, if effectively complemented with each other, may become a powerful tool for predicting the structural class of protein domains.     

A weighting method for predicting protein structural class from amino acid composition.

A protein is generally classified into one of the following four structural classes: all alpha, all beta, alpha+beta and alpha/beta. In this paper, based on the weighting to the 20 constituent amino acids, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The 20 weighting parameters, which reflect the different properties of the 20 constituent amino acids, have been obtained from a training set of proteins through the linear-programming approach. The rate of correct prediction for a training set of proteins by means of the new method was 100%, whereas the highest rate of previous methods was 82.8%. Furthermore, the results showed that the more numerous training proteins, the more effective the new method.     

Classification of Nucleotide Sequences Using Support Vector Machines

Species identification is one of the most important issues in biological studies. Due to recent increases in the amount of genomic information available and the development of DNA sequencing technologies, the applicability of using DNA sequences to identify species (commonly referred to as “DNA barcoding”) is being tested in many areas. Several methods have been suggested to identify species using DNA sequences, including similarity scores, analysis of phylogenetic and population genetic information, and detection of species-specific sequence patterns. Although these methods have demonstrated good performance under a range of circumstances, they also have limitations, as they are subject to loss of information, require intensive computation and are sensitive to model mis-specification, and can be difficult to evaluate in terms of the significance of identification. Here, we suggest a new DNA barcoding method in which support vector machine (SVM) procedures are adopted. Our new method is nonparametric and thus is expected to be robust for a wide range of evolutionary scenarios as well as multilocus analyses. Furthermore, we describe bootstrap procedures that can be used to test the significances of species identifications. We implemented a novel conversion technique for transforming sequence data to real-valued vectors, and therefore, bootstrap procedures can be easily combined with our SVM approach. In this study, we present the results of simulation studies and empirical data analyses to demonstrate the performance of our method and discuss its properties.     

Applying Support Vector Machines for Gene ontology based gene function prediction

Background

The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions.

Results

We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was benchmarked with a large dataset. An organism-wise cross-validation was performed to define confidence estimates, resulting in an average precision of 80% for 74% of all test sequences. The validation results show that the prediction performance was organism-independent and could reproduce the annotation of other automated systems as well as high-quality manual annotations. We applied our trained classification system to Xenopus laevis sequences, yielding functional annotation for more than half of the known expressed genome. Compared to the currently available annotation, we provided more than twice the number of contigs with good quality annotation, and additionally we assigned a confidence value to each predicted GO term.

Conclusions

We present a complete automated annotation system that overcomes many of the usual problems by applying a controlled vocabulary of Gene Ontology and an established classification method on large and well-described sequence data sets. In a case study, the function for Xenopus laevis contig sequences was predicted and the results are publicly available at ftp://genome.dkfz-heidelberg.de/pub/agd/gene_association.agd_Xenopus.

     

Gene Classification Using Codon Usage and Support Vector Machines

Abstract-- A novel approach for gene classification, which adopts codon usage bias as input feature vector for classification by support vector machines (SVM) is proposed. The DNA sequence is first converted to a 59-dimensional feature vector where each element corresponds to the relative synonymous usage frequency of a codon. As the input to the classifier is independent of sequence length and variance, our approach is useful when the sequences to be classified are of different lengths, a condition that homology-based methods tend to fail. The method is demonstrated by using 1,841 Human Leukocyte Antigen (HLA) sequences which are classified into two major classes: HLA-I and HLA-II; each major class is further subdivided into sub-groups of HLA-I and HLA-II molecules. Using codon usage frequencies, binary SVM achieved accuracy rate of 99.3% for HLA major class classification and multi-class SVM achieved accuracy rates of 99.73% and 98.38% for sub-class classification of HLA-I and HLA-II molecules, respectively. The results show that gene classification based on codon usage bias is consistent with the molecular structures and biological functions of HLA molecules.     

基于支持向量机融合网络的蛋白质折叠子识别研究

在不依赖于序列相似性的条件下,蛋白质折叠子识别是一种分析蛋白质结构的重要方法.提出了一种三层支持向量机融合网络,从蛋白质的氨基酸序列出发,对27类折叠子进行识别.融合网络使用支持向量机作为成员分类器,采用“多对多”的多类分类策略,将折叠子的6种特征分为主要特征和次要特征,构建了多个差异的融合方案,然后对这些融合方案进行动态选择得到最终决策.当分类之前难以确定哪些参与组合的特征种类能够使分类结果最好时,提供了一种可靠的解决方案来自动选择特征信息互补最大的组合,保证了最佳分类结果.最后,识别系统对独立测试样本的总分类精度达到61.04%.结果和对比表明,此方法是一种有效的折叠子识别方法.     

Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation

Background  

The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task.     

An optimization approach to predicting protein structural class from amino acid composition.

Proteins are generally classified into four structural classes: all-alpha proteins, all-beta proteins, alpha + beta proteins, and alpha/beta proteins. In this article, a protein is expressed as a vector of 20-dimensional space, in which its 20 components are defined by the composition of its 20 amino acids. Based on this, a new method, the so-called maximum component coefficient method, is proposed for predicting the structural class of a protein according to its amino acid composition. In comparison with the existing methods, the new method yields a higher general accuracy of prediction. Especially for the all-alpha proteins, the rate of correct prediction obtained by the new method is much higher than that by any of the existing methods. For instance, for the 19 all-alpha proteins investigated previously by P.Y. Chou, the rate of correct prediction by means of his method was 84.2%, but the correct rate when predicted with the new method would be 100%! Furthermore, the new method is characterized by an explicable physical picture. This is reflected by the process in which the vector representing a protein to be predicted is decomposed into four component vectors, each of which corresponds to one of the norms of the four protein structural classes.     

Sparse Support Vector Machines with L_{p} Penalty for Biomarker Identification

The development of high-throughput technology has generated a massive amount of high-dimensional data, and many of them are of discrete type. Robust and efficient learning algorithms such as LASSO [1] are required for feature selection and overfitting control. However, most feature selection algorithms are only applicable to the continuous data type. In this paper, we propose a novel method for sparse support vector machines (SVMs) with L_{p} (p ≪ 1) regularization. Efficient algorithms (LpSVM) are developed for learning the classifier that is applicable to high-dimensional data sets with both discrete and continuous data types. The regularization parameters are estimated through maximizing the area under the ROC curve (AUC) of the cross-validation data. Experimental results on protein sequence and SNP data attest to the accuracy, sparsity, and efficiency of the proposed algorithm. Biomarkers identified with our methods are compared with those from other methods in the literature. The software package in Matlab is available upon request.     

一种新的蛋白质结构类预测方法

基于氨基酸的16种分类模型,给出蛋白质序列的派生序列,进而结合加权拟熵和LZ复杂度构造出34维特征向量来表示蛋白质序列。借助于贝叶斯分类器对同源性不超过25%的640数据集进行蛋白质结构类预测,准确度达到71.28%。     

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient solvers based on sequential minimal and chunking optimization. As a further contribution, the primal problem formulation is developed in terms of regularized risk minimization and the hinge loss, revealing the score function to be used in the actual classification of test patterns. We investigate Scatter SVM properties related to generalization ability, computational efficiency, sparsity and sensitivity maps, and report promising results.     

采用最小二乘支持向量机的青霉素发酵过程建模研究

生化过程通常是严重非线性和时变的复杂动态系统,而且重要过程参数缺少在线测量仪表,对其建立机理模型往往非常耗时和困难。采用最小二乘支持向量机(LS_SVM)并以Pensim仿真平台为例对青霉素发酵这一典型生化过程进行建模研究。给出了LS_SVM参数的调整策略和分析结果,建立了青霉素产物浓度、菌体浓度和底物浓度等重要过程变量的在线预报模型。仿真结果表明用LS_SVM建立的在线预报模型拟合误差小,推广性能好,可以作为发酵过程的进一步控制和优化的参考依据。     

A hybrid genetic-neural model for predicting protein structural classes

A genetic algorithm (GA) for feature selection in conjunction with neural network was applied to predict protein structural classes based on single amino acid and all dipeptide composition frequencies. These sequence parameters were encoded as input features for a GA in feature selection procedure and classified with a three-layered neural network to predict protein structural classes. The system was established through optimization of the classification performance of neural network which was used as evaluation function. In this study, self-consistency and jackknife tests on a database containing 498 proteins were used to verify the performance of this hybrid method, and were compared with some of prior works. The adoption of a hybrid model, which encompasses genetic and neural technologies, demonstrated to be a promising approach in the task of protein structural class prediction.     

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

Background  

The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.