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Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

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BMC Research Notes 2014年第1期7卷 1-11页

作者： Kayvanjoo, Amir Hossein Ebrahimi, Mansour Haqshenas, Gholamreza Department of Biology School of Basic Sciences University of Qom Qom Iran Microbiology Department Monash University Melbourne Australia

Background: Hepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world population and remains one of the health threatening human viruses, worldwide. In the absence of an effective vaccine, therapeutic approach is the only option to combat hepatitis C. Interferon-alpha (IFN-alpha) and ribavirin (RBV) combination alone or in combination with recently introduced new direct-acting antivirals (DAA) is used to treat patients infected with HCV. The present study utilized feature selection methods (Gini Index, Chi Squared and machine learning algorithms) and other bioinformatics tools to identify genetic determinants of therapy outcome within the entire HCV nucleotide sequence.;Results: Using combination of several algorithms, the present study performed a comprehensive bioinformatics analysis and identified several nucleotide attributes within the full-length nucleotide sequences of HCV subtypes 1a and 1b that correlated with treatment outcome. Feature selection algorithms identified several nucleotide features (e.g. count of hydrogen and CG). Combination of algorithms utilized the selected nucleotide attributes and predicted HCV subtypes 1a and 1b therapy responders from non-responders with an accuracy of 75.00% and 85.00%, respectively. In addition, therapy responders and relapsers were categorized with an accuracy of 82.50% and 84.17%, respectively. Based on the identified attributes, decision trees were induced to differentiate different therapy response groups.;Conclusions: The present study identified new genetic markers that potentially impact the outcome of hepatitis C treatment. In addition, the results suggest new viral genomic attributes that might influence the outcome of IFN-mediated immune response to HCV infection. © © 2014KayvanJoo et al.;licensee BioMed Central Ltd.

关键词： support vector machine Sustained Virological Response support vector machine model Boceprevir Linear support vector machine

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A novel approach for protein subcellular location prediction using amino acid exposure

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BMC BIOINFORMATICS 2013年第1期14卷 1-13页

作者： Mer, Arvind Singh Andrade-Navarro, Miguel A. Max Delbruck Ctr Mol Med D-13125 Berlin Germany

Background: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. Results: Our method has two stages. For stage one, we trained multiple support vector machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. Conclusions: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem.

关键词： support vector machine Artificial Neural Network Amino Acid Composition support vector machine model Protein Pair

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An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

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BMC BIOINFORMATICS 2013年第1期14卷 1-14页

作者： Zou, Chuanxin Gong, Jiayu Li, Honglin E China Univ Sci & Technol Shanghai Key Lab New Drug Design State Key Lab Bioreactor Engn Sch Pharm Shanghai 200237 Peoples R China

Background: DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence. Results: In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively. Conclusions: The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

关键词： support vector machine support vector machine model Ensemble Learning Protein Function Prediction Dipeptide Composition

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Utilizing support vector machine in real-time crash risk evaluation

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ACCIDENT ANALYSIS AND PREVENTION 2013年第Mar.期51卷 252-259页

作者： Yu, Rongjie Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32826 USA

Real-time crash risk evaluation models will likely play a key role in Active Traffic Management (ATM). models have been developed to predict crash occurrence in order to proactively improve traffic safety. Previous real-time crash risk evaluation studies mainly employed logistic regression and neural network models which have a linear functional form and over-fitting drawbacks, respectively. Moreover, these studies mostly focused on estimating the models but barely investigated the models' predictive abilities. In this study, support vector machine (SVM), a recently proposed statistical learning model was introduced to evaluate real-time crash risk. The data has been split into a training dataset (used for developing the models) and scoring datasets (meant for assessing the models' predictive power). Classification and regression tree (CART) model has been developed to select the most important explanatory variables and based on the results, three candidates Bayesian logistic regression models have been estimated with accounting for different levels unobserved heterogeneity. Then SVM models with different kernel functions have been developed and compared to the Bayesian logistic regression model. model comparisons based on areas under the ROC curve (AUC) demonstrated that the SVM model with Radial-basis kernel function outperformed the others. Moreover, several extension analyses have been conducted to evaluate the effect of sample size on SVM models' predictive capability;the importance of variable selection before developing SVM models;and the effect of the explanatory variables in the SVM models. Results indicate that (1) smaller sample size would enhance the SVM model's classification accuracy, (2) variable selection procedure is needed prior to the SVM model estimation, and (3) explanatory variables have identical effects on crash occurrence for the SVM models and logistic regression models. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： support vector machine model Bayesian logistic regression Real-time crash risk evaluation Mountainous freeway safety

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iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data

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BMC GENOMICS 2013年第2期14卷 1-10页

作者： Sun, Kun Chen, Xiaona Jiang, Peiyong Song, Xiaofeng Wang, Huating Sun, Hao Chinese Univ Hong Kong Li Ka Shing Inst Hlth Sci Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Chem Pathol Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Obstet & Gynaecol Shatin Hong Kong Peoples R China Nanjing Univ Aeronaut & Astronaut Dept Biomed Engn Nanjing 210016 Peoples R China

Background: Long intergenic non-coding RNAs (lincRNAs) are emerging as a novel class of non-coding RNAs and potent gene regulators. High-throughput RNA-sequencing combined with de novo assembly promises quantity discovery of novel transcripts. However, the identification of lincRNAs from thousands of assembled transcripts is still challenging due to the difficulties of separating them from protein coding transcripts (PCTs). Results: We have implemented iSeeRNA, a support vector machine (SVM)-based classifier for the identification of lincRNAs. iSeeRNA shows better performance compared to other software. A public available webserver for iSeeRNA is also provided for small size dataset. Conclusions: iSeeRNA demonstrates high prediction accuracy and runs several magnitudes faster than other similar programs. It can be integrated into the transcriptome data analysis pipelines or run as a web server, thus offering a valuable tool for lincRNA study.

关键词： support vector machine Training Dataset support vector machine model Matthews Correlation Coefficient Conservation Score

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DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels

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GENOME BIOLOGY 2013年第3期14卷 R23-R23页

作者： Zhao, Huiying Yang, Yuedong Lin, Hai Zhang, Xinjun Mort, Matthew Cooper, David N. Liu, Yunlong Zhou, Yaoqi Indiana Univ Purdue Univ Sch Informat Indianapolis IN 46202 USA Indiana Univ Sch Med Ctr Computat Biol & Bioinformat Indianapolis IN 46202 USA Cardiff Univ Inst Med Genet Cardiff CF14 4XN S Glam Wales Indiana Univ Sch Med Dept Med & Mol Genet Indianapolis IN 46202 USA

Micro-indels (insertions or deletions shorter than 21 bps) constitute the second most frequent class of human gene mutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damaging effect on protein structure and function has gone largely unstudied. We have developed a support vector machine-based method named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indels by comparing disease-associated mutations with putatively neutral mutations from the 1,000 Genomes Project. The final model gives good discrimination for indels and is robust against annotation errors. A webserver implementing DDIG-in is available at http://***/ddig.

关键词： support vector machine Relative Entropy support vector machine model Accessible Surface Area Matthews Correlation Coefficient

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Using support vector machine models for crash injury severity analysis

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ACCIDENT ANALYSIS AND PREVENTION 2012年 45卷 478-486页

作者： Li, Zhibin Liu, Pan Wang, Wei Xu, Chengcheng Southeast Univ Sch Transportat Nanjing 210096 Jiangsu Peoples R China

The study presented in this paper investigated the possibility of using support vector machine (SVM) models for crash injury severity analysis. Based on crash data collected at 326 freeway diverge areas, a SVM model was developed for predicting the injury severity associated with individual crashes. An ordered probit (OP) model was also developed using the same dataset. The research team compared the performance of the SVM model and the OP model. It was found that the SVM model produced better prediction performance for crash injury severity than did the OP model. The percent of correct prediction for the SVM model was found to be 48.8%, which was higher than that produced by the OP model (44.0%). Even though the SVM model may suffer from the multi-class classification problem, it still provides better prediction results for small proportion injury severities than the OP model does. The research also investigated the potential of using the SVM model for evaluating the impacts of external factors on crash injury severities. The sensitivity analysis results show that the SVM model produced comparable results regarding the impacts of variables on crash injury severity as compared to the OP model. For several variables such as the length of the exit ramp and the shoulder width of the freeway mainline, the results of the SVM model are more reasonable than those of the OP model. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： support vector machine model Ordered probit model Crash severity Freeway diverge area

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Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

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BMC bioinformatics 2014年第1期15卷 340页

作者： Liqi Li Sanjiu Yu Weidong Xiao Yongsheng Li Lan Huang Xiaoqi Zheng Shiwen Zhou Hua Yang Department of General Surgery Xinqiao Hospital Third Military Medical University Chongqing 400037 China. xqzheng@***.

BACKGROUND:Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed. RESULTS:Here we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools. CONCLUSIONS:Comparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.

关键词： support vector machine Feature Selection support vector machine model Benchmark Dataset Ranking List

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PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors

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BMC BIOINFORMATICS 2013年第14-Sup期14卷 S9-S9页

作者： Ahmed, Firoz Kaundal, Rakesh Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh India Oklahoma State Univ NIMFFAB Dept Biochem & Mol Biol Stillwater OK 74078 USA Samuel Roberts Noble Fdn Inc Div Plant Biol Bioinformat Lab Ardmore OK 73401 USA

Background: Dicer, an RNase III enzyme, plays a vital role in the processing of pre-miRNAs for generating the miRNAs. The structural and sequence features on pre-miRNA which can facilitate position and efficiency of cleavage are not well known. A precise cleavage by Dicer is crucial because an inaccurate processing can produce miRNA with different seed regions which can alter the repertoire of target genes. Results: In this study, a novel method has been developed to predict Dicer cleavage sites on pre-miRNAs using support vector machine. We used the dataset of experimentally validated human miRNA hairpins from miRBase, and extracted fourteen nucleotides around Dicer cleavage sites. We developed number of models using various types of features and achieved maximum accuracy of 66% using binary profile of nucleotide sequence taken from 5p arm of hairpin. The prediction performance of Dicer cleavage site improved significantly from 66% to 86% when we integrated secondary structure information. This indicates that secondary structure plays an important role in the selection of cleavage site. All models were trained and tested on 555 experimentally validated cleavage sites and evaluated using 5-fold cross validation technique. In addition, the performance was also evaluated on an independent testing dataset that achieved an accuracy of similar to 82%. Conclusion: Based on this study, we developed a webserver PHDcleav (http://***/raghava/phdcleav/) to predict Dicer cleavage sites in pre-miRNA. This tool can be used to investigate functional consequences of genetic variations/SNPs in miRNA on Dicer cleavage site, and gene silencing. Moreover, it would also be useful in the discovery of miRNAs in human genome and design of Dicer specific pre-miRNAs for potent gene silencing.

关键词： support vector machine Cleavage Site support vector machine model Negative Pattern Independent Testing Dataset

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iStable: off-the-shelf predictor integration for predicting protein stability changes

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BMC BIOINFORMATICS 2013年第2-sup期14卷 1-14页

作者： Chen, Chi-Wei Lin, Jerome Chu, Yen-Wei Natl Chung Hsing Univ Inst Genom & Bioinformat Taichung 402 Taiwan Natl Chung Hsing Univ Ctr Biotechnol Taichung 402 Taiwan Natl Chung Hsing Univ Agr Biotechnol Ctr Taichung 402 Taiwan Natl Chung Hsing Univ Inst Mol Biol Taichung 402 Taiwan Natl Chung Hsing Univ Grad Inst Biotechnol Taichung 402 Taiwan

Background: Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users. Results: We proposed an integrated predictor, iStable, with grid computing architecture constructed by using sequence information and prediction results from different element predictors. In the learning model, several machine learning methods were evaluated and adopted the support vector machine as an integrator, while not just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available with two different input types: structural and sequential. After training and cross-validation, iStable has better performance than all of the element predictors on several datasets. Under different classifications and conditions for validation, this study has also shown better overall performance in different types of secondary structures, relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental conditions. Conclusions: The trained and validated version of iStable provides an accurate approach for prediction of protein stability changes. iStable is freely available online at: http://***/iStable.

关键词： support vector machine Protein Data Bank support vector machine model Matthews Correlation Coefficient Stability Change

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