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SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

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BMC BIOINFORMATICS 2008年第1期9卷 226-226页

作者： Kurgan, Lukasz Cios, Krzysztof Chen, Ke Univ Alberta Dept Elect & Comp Engn ECEFR Edmonton AB T6G 2V4 Canada Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

Background: Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight- zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight- zone pairwise similarity with sequences used for the prediction. Results: SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight- zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion: The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the desig

关键词： support vector machine Secondary Structure Feature vector Protein Data Bank support vector machine classifier

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Combining classifiers for improved classification of proteins from sequence or structure

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BMC BIOINFORMATICS 2008年第1期9卷 389-389页

作者： Melvin, Iain Weston, Jason Leslie, Christina S. Noble, William S. Univ Washington Dept Comp Sci & Engn Dept Genome Sci Seattle WA 98195 USA NEC Labs Amer Princeton NJ USA Mem Sloan Kettering Canc Ctr Sloan Kettering Inst Computat Biol Program New York NY 10021 USA

Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage. Results: In this study, we develop a hybrid machine learning approach for classifying proteins, and we apply the method to the problem of assigning proteins to structural categories based on their sequences or their 3D structures. The method combines a full-coverage but lower accuracy nearest neighbor method with higher accuracy but reduced coverage multiclass SVMs to produce a full coverage classifier with overall improved accuracy. The hybrid approach is based on the simple idea of "punting" from one method to another using a learned threshold. Conclusion: In cross-validated experiments on the SCOP hierarchy, the hybrid methods consistently outperform the individual component methods at all levels of coverage.

关键词： support vector machine support vector machine classifier Near Neighbor Hybrid classifier Multiclass support vector machine

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Automated breast cancer classification using near-infrared optical tomographic images

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JOURNAL OF BIOMEDICAL OPTICS 2008年第4期13卷 44001-44001页

作者： Wang, James Z. Liang, Xiaoping Zhang, Qizhi Fajardo, Laurie L. Jiang, Huabei Univ Florida J Crayton Pruitt Family Dept Biomed Engn Gainesville FL 32611 USA Clemson Univ Sch Comp Clemson SC 29634 USA Univ Iowa Dept Radiol Iowa City IA 52242 USA

An automated procedure for detecting breast cancer using near-infrared (NIR) tomographic images is presented. This classification procedure automatically extracts attributes from three imaging parameters obtained by an NIR imaging system. These parameters include tissue absorption and reduced scattering coefficients, as well as a tissue refractive index obtained by a phase-contrast-based reconstruction approach. A support vector machine (SVM) classifier is utilized to distinguish the malignant from the benign lesions using the automatically extracted attributes. The classification results of in vivo tomographic images from 35 breast masses using absorption, scattering, and refractive index attributes demonstrate high sensitivity, specificity, and overall accuracy of 81.8%, 91.7%, and 88.6% respectively, while the classification sensitivity, specificity, and overall accuracy are 63.6%, 83.3%, and 77.1%, respectively, when only the absorption and scattering attributes are used. Furthermore, the automated classification procedure provides significantly improved specificity and overall accuracy for breast cancer detection compared to those by an experienced technician through visual examination. (C) 2008 Society of Photo-Optical Instrumentation Engineers.

关键词： phase-contrast diffuse optical tomography refractive index image absorption coefficient image scattering coefficient image support vector machine classifier image segmentation feature extraction

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Computational identification of ubiquitylation sites from protein sequences

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BMC BIOINFORMATICS 2008年第1期9卷 310-310页

作者： Tung, Chun-Wei Ho, Shinn-Ying Natl Chiao Tung Univ Inst Bioinformat Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Biol Sci & Technol Hsinchu 300 Taiwan

Background: Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites. Results: We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, k-nearest neighbor, and NaiveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation. Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules. Conclusion: We have prop

关键词： support vector machine Prediction Accuracy Training Dataset Amino Acid Identity support vector machine classifier

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Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

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BMC BIOINFORMATICS 2008年第1期9卷 203-203页

作者： Zhang, Yuji Xuan, Jianhua de los Reyes, Benilo G. Clarke, Robert Ressom, Habtom W. Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA USA Univ Maine Sch Biol & Ecol Orono ME 04469 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC USA

Background: Integrating data from multiple global assays and curated databases is essential to understand the spatiotemporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information. Results: The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate th

关键词： Gene Ontology Root Mean Square Error support vector machine classifier Recurrent Neural Network Network Motif

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Automatic detection of exonic splicing enhancers (ESEs) using SVMs

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BMC BIOINFORMATICS 2008年第1期9卷 1-15页

作者： Mersch, Britta Gepperth, Alexander Suhai, Sandor Hotz-Wagenblatt, Agnes German Canc Res Ctr Dept Mol Biophys D-69120 Heidelberg Germany Honda Res Inst Europe GmbH D-63073 Offenbach Germany

Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins. Results: The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel) can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters. Conclusion: The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

关键词： support vector machine Splice Site support vector machine classifier Markov Chain Model Exonic Splice Enhancer

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ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization

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BMC BIOINFORMATICS 2008年第1期9卷 1-16页

作者： Huang, Wen-Lin Tung, Chun-Wei Ho, Shih-Wen Hwang, Shiow-Fen Ho, Shinn-Ying Natl Chiao Tung Univ Inst Bioinformat Hsinchu Taiwan Feng Chia Univ Inst Comp Sci & Informat Engn Taichung 40724 Taiwan Chin Min Inst Technol Dept Management Informat Syst Miaoli Taiwan Natl Chiao Tung Univ Dept Biol Sci & Technol Hsinchu Taiwan

Background: Gene Ontology (GO) annotation, which describes the function of genes and gene products across species, has recently been used to predict protein subcellular and subnuclear localization. Existing GO-based prediction methods for protein subcellular localization use the known accession numbers of query proteins to obtain their annotated GO terms. An accurate prediction method for predicting subcellular localization of novel proteins without known accession numbers, using only the input sequence, is worth developing. Results: This study proposes an efficient sequence-based method (named ProLoc-GO) by mining informative GO terms for predicting protein subcellular localization. For each protein, BLAST is used to obtain a homology with a known accession number to the protein for retrieving the GO annotation. A large number n of all annotated GO terms that have ever appeared are then obtained from a large set of training proteins. A novel genetic algorithm based method (named GOmining) combined with a classifier of support vector machine (SVM) is proposed to simultaneously identify a small number m out of the n GO terms as input features to SVM, where m << n. The m informative GO terms contain the essential GO terms annotating subcellular compartments such as GO: 0005634 (Nucleus), GO: 0005737 (Cytoplasm) and GO: 0005856 (Cytoskeleton). Two existing data sets SCL12 (human protein with 12 locations) and SCL16 (Eukaryotic proteins with 16 locations) with < 25% sequence identity are used to evaluate ProLoc-GO which has been implemented by using a single SVM classifier with the m = 44 and m = 60 informative GO terms, respectively. ProLoc-GO using input sequences yields test accuracies of 88.1% and 83.3% for SCL12 and SCL16, respectively, which are significantly better than the SVM-based methods, which achieve < 35% test accuracies using amino acid composition (AAC) with acid pairs and AAC with dipedtide composition. For comparison, ProLoc-GO using known accession nu

关键词： support vector machine Gene Ontology support vector machine classifier Ensemble classifier Matthews Correlation Coefficient

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Glycosylation site prediction using ensembles of support vector machine classifiers

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BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Caragea, Cornelia Sinapov, Jivko Silvescu, Adrian Dobbs, Drena Honavar, Vasant Iowa State Univ Dept Comp Sci Artificial Intelligence Res Lab Ames IA 50011 USA Iowa State Univ Ctr Comp Intelligence Learning & Discovery Ames IA 50011 USA Iowa State Univ Dept Genet Dev & Cell Biol Ames IA 50011 USA Iowa State Univ Bioinformat & Computat Biol Ames IA 50011 USA

Background: Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. Results: We explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of support vector machine classifiers and ensembles of support vector machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of support vector machine classifiers outperform single support vector machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction. Conclusion: Ensembles of support vector machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.

关键词： support vector machine Receiver Operating Characteristic Curve Glycosylation Site support vector machine classifier Minority Class

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A comparative study of different machine learning methods on microarray gene expression data

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BMC GENOMICS 2008年第s1期9卷 S13-S13页

作者： Pirooznia, Mehdi Yang, Jack Y. Yang, Mary Qu Deng, Youping Univ So Mississippi Dept Biol Sci Hattiesburg MS 39406 USA Harvard Univ Sch Med Cambridge MA 02140 USA NHGRI NIH US Dept Hlth & Human Serv Bethesda MD 20852 USA

Background: Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results. Results: In this study, we compared the efficiency of the classification methods including;SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers. Conclusions: We presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed

关键词： support vector machine Feature Selection Radial Basis Function Expectation Maximization support vector machine classifier

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HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels

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RETROVIROLOGY 2008年第1期5卷 110-110页

作者： Boisvert, Sebastien Marchand, Mario Laviolette, Francois Corbeil, Jacques CHU Laval Ctr Rech Quebec City PQ G1V 4G2 Canada Univ Laval Dept Informat & Genie Logiciel Quebec City PQ Canada

Background: Human immunodeficiency virus type 1 (HIV-1) infects cells by means of ligand-receptor interactions. This lentivirus uses the CD4 receptor in conjunction with a chemokine coreceptor, either CXCR4 or CCR5, to enter a target cell. HIV-1 is characterized by high sequence variability. Nonetheless, within this extensive variability, certain features must be conserved to define functions and phenotypes. The determination of coreceptor usage of HIV-1, from its protein envelope sequence, falls into a well-studied machine learning problem known as classification. The support vector machine (SVM), with string kernels, has proven to be very efficient for dealing with a wide class of classification problems ranging from text categorization to protein homology detection. In this paper, we investigate how the SVM can predict HIV-1 coreceptor usage when it is equipped with an appropriate string kernel. Results: Three string kernels were compared. Accuracies of 96.35% (CCR5) 94.80% (CXCR4) and 95.15% (CCR5 and CXCR4) were achieved with the SVM equipped with the distant segments kernel on a test set of 1425 examples with a classifier built on a training set of 1425 examples. Our datasets are built with Los Alamos National Laboratory HIV Databases sequences. A web server is available at http://***/hiv- dskernel. Conclusion: We examined string kernels that have been used successfully for protein homology detection and propose a new one that we call the distant segments kernel. We also show how to extract the most relevant features for HIV-1 coreceptor usage. The SVM with the distant segments kernel is currently the best method described.

关键词： support vector machine support vector machine classifier Coreceptor Usage String Kernel Discriminant vector

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