检索结果-内蒙古大学图书馆

Identification of ATP binding residues of a protein from its primary sequence

BMC BIOINFORMATICS 2009年第1期10卷 434-434页

作者： Chauhan, Jagat S. Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol Chandigarh India

Background: One of the major challenges in post-genomic era is to provide functional annotations for large number of proteins arising from genome sequencing projects. The function of many proteins depends on their interaction with small molecules or ligands. ATP is one such important ligand that plays critical role as a coenzyme in the functionality of many proteins. There is a need to develop method for identifying ATP interacting residues in a ATP binding proteins (ABPs), in order to understand mechanism of protein-ligands interaction. Results: We have compared the amino acid composition of ATP interacting and non-interacting regions of proteins and observed that certain residues are preferred for interaction with ATP. This study describes few models that have been developed for identifying ATP interacting residues in a protein. All these models were trained and tested on 168 non-redundant ABPs chains. First we have developed a support vector machine (SVM) based model using primary sequence of proteins and obtained maximum MCC 0.33 with accuracy of 66.25%. Secondly, another SVM based model was developed using position specific scoring matrix (PSSM) generated by PSI-BLAST. The performance of this model was improved significantly (MCC 0.5) from the previous one, where only the primary sequence of the proteins were used. Conclusion: This study demonstrates that it is possible to predict 'ATP interacting residues' in a protein with moderate accuracy using its sequence. The evolutionary information is important for the identification of 'ATP interacting residues', as it provides more information compared to the primary sequence. This method will be useful for researchers studying ATP-binding proteins. Based on this study, a web server has been developed for predicting 'ATP interacting residues' in a protein http://***/raghava/atpint/.

关键词： support vector machine Window Size support vector machine model Evolutionary Information Interact Residue

来源：评论

学校读者我要写书评

暂无评论

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

引用

CHEMISTRY CENTRAL JOURNAL 2008年第1期2卷 1-15页

作者： O'Boyle, Noel M. Palmer, David S. Nigsch, Florian Mitchell, John B. O. Univ Cambridge Dept Chem Unilever Ctr Mol Sci Informat Cambridge CB2 1EW England Cambridge Crystallog Data Ctr Cambridge CB2 1EZ England Aarhus Univ Dept Chem DK-8000 Aarhus Denmark

Background: We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf model 2005, 45:1024-1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf model 2005, 45:581-590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results: Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6 degrees C and R-2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, epsilon of 0.21) and an RMSE of 45.1 degrees C and R-2 of 0.54. This model outperforms a kNN model (RMSE of 48.3 degrees C, R-2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5 degrees C, R-2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion: With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descri

关键词： support vector machine Feature Selection Partial Little Square Random Forest support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

引用

BMC BIOINFORMATICS 2007年第1期8卷 1-29页

作者： Peek, Andrew S. Integrated DNA Technol Inc Dept Bioinformat Coralville IA 52241 USA

Background: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel support vector machine (SVM) approach was used to quantitatively model RNA interference activities. Results: Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion: The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at

关键词： support vector machine Mean Square Error Feature Subset support vector machine model siRNA Sequence

来源：评论

学校读者我要写书评

暂无评论

GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

引用

BMC BIOINFORMATICS 2008年第1期9卷 1-9页

作者： Yu, Wei Clyne, Melinda Dolan, Siobhan M. Yesupriya, Ajay Wulf, Anja Liu, Tiebin Khoury, Muin J. Gwinn, Marta Ctr Dis Control & Prevent Natl Off Publ Hlth Genom Coordinating Ctr Hlth Promot Atlanta GA USA Montefiore Med Ctr Albert Einstein Coll Med Bronx NY 10467 USA

Background: Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The support vector machine ( SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies. Results: The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy. Conclusion: GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.

关键词： support vector machine Preterm Birth support vector machine model Unify Medical Language System PubMed Abstract

来源：评论

学校读者我要写书评

暂无评论

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

引用

BMC BIOINFORMATICS 2008年第1期9卷 503-503页

作者： Garg, Aarti Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh India Panjab Univ Dept Biotechnol Chandigarh 160014 India

Background: The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes;hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features. Results: Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to support vector machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets;1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after fivefold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively;best accuracies reported till date on the same datasets. Conclusion: These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently deve

关键词： support vector machine Average Accuracy support vector machine model Input Feature Evolutionary Information

来源：评论

学校读者我要写书评

暂无评论

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

引用

BMC BIOINFORMATICS 2008年第s1期9卷 S19-S19页

作者： Zhang, Guang Lan Khan, Asif M. Srinivasan, Kellathur N. Heiny, A. T. Lee, K. X. Kwoh, Chee Keong August, J. Thomas Brusic, Vladimir Inst Infocomm Res Singapore 119613 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Natl Univ Singapore Yong Loo Lin Sch Med Dept Biochem Singapore 117597 Singapore Natl Univ Singapore Yong Loo Lin Sch Med Dept Microbiol Singapore 117597 Singapore Johns Hopkins Sch Med Dept Pharmacol & Mol Sci Baltimore MD 21205 USA Hlth Sci Author Ctr Drug Adm Prod Evaluat & Registrat Div Singapore 138667 Singapore Dana Farber Canc Inst Canc Vaccine Ctr Boston MA 02115 USA Univ Queensland Sch Land Crop & Food Sci Brisbane Qld 4072 Australia

Background: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. Results: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and support vector machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the fo

关键词： Human Leukocyte Antigen support vector machine model Human Leukocyte Antigen Class Myelin Oligodendrocyte Glycoprotein NCBI Accession

来源：评论

学校读者我要写书评

暂无评论

Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles

引用

BMC BIOINFORMATICS 2008年第s1期9卷 1-10页

作者： Tsai, Richard Tzong-Han Hung, Hsi-Chuan Dai, Hong-Jie Lin, Yi-Wen Hsu, Wen-Lian Yuan Ze Univ Dept Comp Sci & Engn Tao Yuan 32003 Taiwan Acad Sinica Inst Informat Sci Taipei 115 Taiwan

Background: Experimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles' relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles. Results: To evaluate the performance of our PPI text classifier, we conducted experiments based on the BioCreAtIvE-II IAS dataset. Our results show that adding likely-labeled data generally increases AUC by 3 similar to 6%, indicating better ranking ability. Our experiments also show that our newly-proposed term-weighting scheme has the highest AUC among all common weighting schemes. Our final model achieves an F-measure and AUC 2.9% and 5.0% higher than those of the top-ranking system in the IAS challenge. Conclusion: Our experiments demonstrate the effectiveness of integrating unlabele

关键词： support vector machine Weighting Scheme support vector machine model Text Classification Unlabeled Data

来源：评论

学校读者我要写书评

暂无评论

Identification of proteins secreted by malaria parasite into erythrocyte using SVM and PSSM profiles

引用

BMC BIOINFORMATICS 2008年第1期9卷 201-201页

作者： Verma, Ruchi Tiwari, Ajit Kaur, Sukhwinder Varshney, Grish C. Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh India

Background: Malaria parasite secretes various proteins in infected RBC for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine/ drug against malaria. The existing motif-based methods have got limited success due to lack of universal motif in all secretory proteins of malaria parasite. Results: In this study a systematic attempt has been made to develop a general method for predicting secretory proteins of malaria parasite. All models were trained and tested on a non-redundant dataset of 252 secretory and 252 non-secretory proteins. We developed SVM models and achieved maximum MCC 0.72 with 85.65% accuracy and MCC 0.74 with 86.45% accuracy using amino acid and dipeptide composition respectively. SVM models were developed using split-amino acid and split-dipeptide composition and achieved maximum MCC 0.74 with 86.40% accuracy and MCC 0.77 with accuracy 88.22% respectively. In this study, for the first time PSSM profiles obtained from PSI-BLAST, have been used for predicting secretory proteins. We achieved maximum MCC 0.86 with 92.66% accuracy using PSSM based SVM model. All models developed in this study were evaluated using 5-fold cross-validation technique. Conclusion: This study demonstrates that secretory proteins have different residue composition than non-secretory proteins. Thus, it is possible to predict secretory proteins from its residue composition-using machine learning technique. The multiple sequence alignment provides more information than sequence itself. Thus performance of method based on PSSM profile is more accurate than method based on sequence composition. A web server PSEApred has been developed for predicting secretory proteins of malaria parasites, the URL can be found in the Availability and requirements section.

关键词： support vector machine Amino Acid Composition Secretory Protein support vector machine model Position Specific Score Matrix

来源：评论

学校读者我要写书评

暂无评论

Identification of novel and candidate miRNAs in rice by high throughput sequencing

引用

BMC PLANT BIOLOGY 2008年第1期8卷 25-25页

作者： Sunkar, Ramanjulu Zhou, Xuefeng Zheng, Yun Zhang, Weixiong Zhu, Jian-Kang Oklahoma State Univ Dept Biochem & Mol Biol Stillwater OK 74078 USA Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA Univ Calif Riverside Dept Bot & Plant Sci Riverside CA 92521 USA

Background: Small RNA-guided gene silencing at the transcriptional and post-transcriptional levels has emerged as an important mode of gene regulation in plants and animals. Thus far, conventional sequencing of small RNA libraries from rice led to the identification of most of the conserved miRNAs. Deep sequencing of small RNA libraries is an effective approach to uncover rare and lineage- and/or species-specific microRNAs (miRNAs) in any organism. Results: In order to identify new miRNAs and possibly abiotic-stress regulated small RNAs in rice, three small RNA libraries were constructed from control rice seedlings and seedlings exposed to drought or salt stress, and then subjected to pyrosequencing. A total of 58,781, 43,003 and 80,990 unique genome-matching small RNAs were obtained from the control, drought and salt stress libraries, respectively. Sequence analysis confirmed the expression of most of the conserved miRNAs in rice. Importantly, 23 new miRNAs mostly each derived from a unique locus in rice genome were identified. Six of the new miRNAs are conserved in other monocots. Additionally, we identified 40 candidate miRNAs. Allowing not more than 3 mis-matches between a miRNA and its target mRNA, we predicted 20 targets for 9 of the new miRNAs. Conclusion: Deep sequencing proved to be an effective strategy that allowed the discovery of 23 low-abundance new miRNAs and 40 candidate miRNAs in rice.

关键词： Rice Genome Rice Seedling support vector machine model miRNA Family Candidate miRNAs

来源：评论

学校读者我要写书评

暂无评论

Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs

引用

BMC BIOINFORMATICS 2008年第1期9卷 101-101页

作者： Chen, Yong-Zi Tang, Yu-Rong Sheng, Zhi-Ya Zhang, Ziding China Agr Univ Coll Biol Sci Bioinformat Ctr Beijing 100094 Peoples R China Natl Inst Biol Sci Beijing 102206 Peoples R China

Background: As one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins. Results: A new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O- glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O- glycosylation dataset with the assistance of support vector machine (SVM). When the ratio of O- glycosylation to non-glycosylation sites in training datasets was set as 1: 1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O- glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1: 5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i. e. S+T predictor). Either in 1: 1 or 1: 5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O- glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors. Conclusion: Because of CKSAAP encoding's ability

关键词： support vector machine Encode Scheme support vector machine model Matthew Correlation Coefficient support vector machine Algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：