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Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

BMC BIOINFORMATICS 2006年第1期7卷 124-124页

作者： Song, JN Burrage, K Yuan, Z Huber, T Univ Queensland Adv Computat Modelling Ctr Brisbane Qld 4072 Australia Univ Queensland Inst Mol Biosci Brisbane Qld 4072 Australia Univ Queensland ARC Ctr Bioinformat Brisbane Qld 4072 Australia

Background: The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/ trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/ trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results: In this paper, we propose a new approach to predict the proline cis/ trans isomerization in proteins using support vector machine ( SVM). The preliminary results indicated that using Radial Basis Function ( RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI- BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non- homologous proteins determined by X- Ray diffraction method using 5- fold cross- validation. Selecting the window size 11 provided the best performance for determining the proline cis/ trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI- BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient ( MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% a

关键词： support vector machine Prediction Performance support vector machine Classifier support vector machine model Radial Basis Function Kernel

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machine learning techniques in disease forecasting: a case study on rice blast prediction

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BMC BIOINFORMATICS 2006年第1期7卷 485-485页

作者： Kaundal, Rakesh Kapoor, Amar S. Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh 160036 India Himachal Pradesh Agr Univ CSK Dept Plant Pathol Palampur 176062 Himachal Prades India

Background: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results: Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year) were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG) approach achieved an average correlation coefficient (r) of 0.50, which increased to 0.60 and percent mean absolute error (% MAE) decreased from 65.42 to 52.24 when back-propagation neural network (BPNN) was used. With generalized regression neural network (GRNN), the r increased to 0.70 and % MAE also improved to 46.30, which further increased to r = 0.77 and % MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding % MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in % MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion: Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also developed a SVM

关键词： support vector machine support vector machine model Rice Blast Weather Variable Generalize Regression Neural Network

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Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method

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BMC BIOINFORMATICS 2006年第1期7卷 399-399页

作者： Takahashi, Hiro Nemoto, Takeshi Yoshida, Teruhiko Honda, Hiroyuki Hasegawa, Tadashi Nagoya Univ Sch Engn Dept Biotechnol Chikusa Ku Nagoya Aichi 4648603 Japan Natl Canc Ctr Res Inst Div Genet Chuo Ku Tokyo 1040045 Japan Natl Canc Ctr Res Inst Div Pathol Chuo Ku Tokyo 1040045 Japan Sapporo Med Univ Sch Med Dept Surg Pathol Chuo Ku Sapporo Hokkaido 0608543 Japan

Background: Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis. Results: Previously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting - the PART-BFCS method - showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method - MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3 - are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS. Conclusion: The procedure that consisted of two steps, such as the PART- BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by a procedure that includes the PA

关键词： support vector machine Soft Tissue Sarcoma support vector machine model Malignant Fibrous Histiocytoma Fuzzy Neural Network

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support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition

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BMC BIOINFORMATICS 2006年第1期7卷 223-223页

作者： Zhou, Tong Weng, Jianhong Sun, Xiao Lu, Zuhong SE Univ State Key Lab Bioelect Nanjing 210096 Peoples R China

Background: Meiotic double-strand breaks occur at relatively high frequencies in some genomic regions ( hotspots) and relatively low frequencies in others ( coldspots). Hotspots and coldspots are receiving increasing attention in research into the mechanism of meiotic recombination. However, predicting hotspots and coldspots from DNA sequence information is still a challenging task. Results: We present a novel method for classification of hot and cold ORFs located in hotspots and coldspots respectively in Saccharomyces cerevisiae, using support vector machine ( SVM), which relies on codon composition differences. This method has achieved a high classification accuracy of 85.0%. Since codon composition is a fusion of codon usage bias and amino acid composition signals, the ability of these two kinds of sequence attributes to discriminate hot ORFs from cold ORFs was also investigated separately. Our results indicate that neither codon usage bias nor amino acid composition taken separately performed as well as codon composition. Moreover, our SVM based method was applied to the full genome: We predicted the hot/cold ORFs from the yeast genome by using cutoffs of recombination rate. We found that the performance of our method for predicting cold ORFs is not as good as that for predicting hot ORFs. Besides, we also observed a considerable correlation between meiotic recombination rate and amino acid composition of certain residues, which probably reflects the structural and functional dissimilarity between the hot and cold groups. Conclusion: We have introduced a SVM-based novel method to discriminate hot ORFs from cold ones. Applying codon composition as sequence attributes, we have achieved a high classification accuracy, which suggests that codon composition has strong potential to be used as sequence attributes in the prediction of hot and cold ORFs.

关键词： support vector machine Amino Acid Composition Recombination Rate Codon Usage support vector machine model

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Demonstration of two novel methods for predicting functional siRNA efficiency

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BMC BIOINFORMATICS 2006年第1期7卷 271-271页

作者： Jia, Peilin Shi, Tieliu Cai, Yudong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci Bioinformat Ctr Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Partner Inst Computat Biol CAS MPG Shanghai Peoples R China Shanghai Jiao Tong Univ Life Sci Sch Shanghai Peoples R China Univ Manchester Dept Biomol Sci Manchester M60 1QD Lancs England

Background: siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs. Results: In this paper, two systems have been established for the prediction of functional siRNAs: (1) a statistical model based on sequence information and (2) a machine learning model based on three features of siRNA sequences, namely binary description, thermodynamic profile and nucleotide composition. Both of the two methods show high performance on the two datasets we have constructed for training the model. Conclusion: Both of the two methods studied in this paper emphasize the importance of sequence information for the prediction of functional siRNAs. The way of denoting a bio-sequence by binary system in mathematical language might be helpful in other analysis work associated with fixed-length bio-sequence.

关键词： support vector machine support vector machine model Nucleotide Composition support vector machine Algorithm support vector machine Method

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Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein

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BMC BIOINFORMATICS 2005年第1期6卷 1-14页

作者： Raghava, GPS Han, JH Pohang Univ Sci & Technol Dept Comp Sci & Engn Pohang 790784 South Korea Inst Microbial Technol Bioinformat Ctr Chandigarh 160036 India

Background: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., ( 1998) to understand the relationship between expression level and amino acid composition. Results: We compute the correlation between expression of a gene and amino acid composition of its protein. It was observed that some residues ( like Ala, Gly, Arg and Val) have significant positive correlation ( r > 0.20) and some other residues ( Like Asp, Leu, Asn and Ser) have negative correlation ( r < - 0.15) with the expression of genes. A significant negative correlation ( r = - 0.18) was also found between length and gene expression. These observations indicate the relationship between percent composition and gene expression level. Thus, attempts have been made to develop a support vector machine ( SVM) based method for predicting the expression level of genes from its protein sequence. In this method the SVM is trained with proteins whose gene expression data is known in a given condition. Then trained SVM is used to predict the gene expression of other proteins of the same organism in the same condition. A correlation coefficient r = 0.70 was obtained between predicted and experimentally determined expression of genes, which improves from r = 0.70 to 0.72 when dipeptide composition was used instead of residue composition. The method was evaluated using 5-fold cross validation test. We also demonstrate that amino acid composition information along with gene expression data can be used for improving the fu

关键词： support vector machine Amino Acid Composition support vector machine model Percent Composition Reference Dataset

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An SVM-based system for predicting protein subnuclear localizations

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BMC BIOINFORMATICS 2005年第1期6卷 291-291页

作者： Lei, ZD Dai, Y Univ Illinois Dept Bioengn MC063 Chicago IL 60607 USA

Background: The large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computational tool for the prediction of subnuclear and subcellular localizations generally applicable to protein sequences. The information on localization may reveal the molecular function of novel proteins, in addition to providing insight on the biological pathways in which they function. The bulk of past work has been focused on protein subcellular localizations. Furthermore, no specific tool has been dedicated to prediction at the subnuclear level, despite its high importance. In order to design a suitable predictive system, the extraction of subtle sequence signals that can discriminate among proteins with different subnuclear localizations is the key. Results: New kernel functions used in a support vector machine (SVM) learning model are introduced for the measurement of sequence similarity. The k-peptide vectors are first mapped by a matrix of high-scored pairs of k-peptides which are measured by BLOSUM62 scores. The kernels, measuring the similarity for sequences, are then defined on the mapped vectors. By combining these new encoding methods, a multi-class classification system for the prediction of protein subnuclear localizations is established for the first time. The performance of the system is evaluated with a set of proteins collected in the Nuclear Protein Database (NPD). The overall accuracy of prediction for 6 localizations is about 50% ( vs. random prediction 16.7%) for single localization proteins in the leave-one-out cross-validation;and 65% for an independent set of multi-localization proteins. This integrated system can be accessed at http://***/***. Conclusion: The integrated system benefits from the combination of predictions from several SVMs based on selected encoding methods. Finally, the predictive power of the system is expected to improve

关键词： support vector machine model Encode Method Protein Subcellular Localization Subnuclear Localization Subnuclear Compartment

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Building a protein name dictionary from full text: a machine learning term extraction approach

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BMC BIOINFORMATICS 2005年第1期6卷 88-88页

作者： Shi, L Campagne, F Weill Cornell Med Coll Inst Computat Biomed New York NY 10021 USA Weill Cornell Med Coll Dept Physiol & Biophys New York NY 10021 USA

Background: The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. Results: We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. Conclusion: This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

关键词： support vector machine Full Text support vector machine model Full Text Article Biological Entity

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Entropy-based gene ranking without selection bias for the predictive classification of microarray data

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BMC BIOINFORMATICS 2003年第1期4卷 54-54页

作者： Furlanello, C Serafini, M Merler, S Jurman, G ITC Irst Trento Italy

Background: We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). Results: With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions: Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

关键词： support vector machine support vector machine Classifier support vector machine model Feature Ranking Recursive Feature Elimination

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support vector machines for predicting protein structural class

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BMC BIOINFORMATICS 2001年第3期2卷 3-3页

作者： Cai, Yu-Dong Liu, Xiao-Jun Xu, Xue-biao Zhou, Guo-Ping Chinese Acad Sci Shanghai Res Ctr Biotechnol Shanghai 200233 Peoples R China Univ Edinburgh Inst Cell Anim & Populat Biol Edinburgh EH9 3JT Midlothian Scotland Univ Wales Coll Cardiff Coll Cardiff Dept Comp Sci Cardiff CF2 3XF S Glam Wales Burnham Inst Dept Biol Struct La Jolla CA 92037 USA

Background: We apply a new machine learning method, the so-called support vector machine method, to predict the protein structural class. support vector machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure. Results: High rates of both self-consistency and jackknife tests are obtained. The good results indicate that the structural class of a protein is considerably correlated with its amino acid composition. Conclusions: It is expected that the support vector machine method and the elegant component-coupled method, also named as the covariant discrimination algorithm, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins.

关键词： support vector machine Amino Acid Composition support vector machine model Neural Network Method support vector machine Method

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