Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112366
Yufeng Shen, Y. Cheung, Shuang Wang, I. Pe’er
Testing statistical association of individual rare variants is underpowered due to low frequency. A common approach is to test the aggregated effects of individual variants in a locus such as genes. Current methods have distinct power profiles that are determined by underlying assumptions about the genetic model and effect size. Here we describe a parametric Bayesian approach to detect the association of rare variants. We express the assumptions about effect size by setting the prior distribution in the model, which can be adjusted based on the experimental design. This flexibility allows our method to achieve optimal power. The algorithmic contribution includes a dynamic program for efficient calculation of the association test statistic. We tested the method in simulated data, and demonstrated that it is better powered to detect rare variant association under various scenarios.
{"title":"A parametric Bayesian method to test the association of rare variants","authors":"Yufeng Shen, Y. Cheung, Shuang Wang, I. Pe’er","doi":"10.1109/BIBMW.2011.6112366","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112366","url":null,"abstract":"Testing statistical association of individual rare variants is underpowered due to low frequency. A common approach is to test the aggregated effects of individual variants in a locus such as genes. Current methods have distinct power profiles that are determined by underlying assumptions about the genetic model and effect size. Here we describe a parametric Bayesian approach to detect the association of rare variants. We express the assumptions about effect size by setting the prior distribution in the model, which can be adjusted based on the experimental design. This flexibility allows our method to achieve optimal power. The algorithmic contribution includes a dynamic program for efficient calculation of the association test statistic. We tested the method in simulated data, and demonstrated that it is better powered to detect rare variant association under various scenarios.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"81 1","pages":"137-143"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83402315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112462
Ruizhi Zhao, You-jun Chen, Jian Cai
In traditional Chinese medicine co-administration drugs with vinegar-baked Radix Bupleuri (VBRB) is usually used to increase the therapeutic effect in liver disease. However, the scientific data for this effect are not available. In this paper, effect of VBRB on the distribution of oxymatrine was studied. Mice were divided into four groups by random, oxymatrine control and oxymatrine co-administered with three different doses of VBRB. Concentrations of oxymatrine and its metabolite matrine in different tissues were determined by HPLC-MS. Target effencicy was evalutaed by AUC, Cmax, and Relative targeting efficiency (RTE). The results showed that compared to the control group, VBRB significantly increased the distribution of both oxymatrine and matrine in liver and meanwhile decreased their distribution in other tissues, indicating a strong liver targeting enhancing effect. The results of this paper implied that co-administration with VBRB may be a simple and efficiencient method for liver targeting therapy.
{"title":"Liver targeting effect of vinegar-baked Radix Bupleuri on oxymatrine in mice","authors":"Ruizhi Zhao, You-jun Chen, Jian Cai","doi":"10.1109/BIBMW.2011.6112462","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112462","url":null,"abstract":"In traditional Chinese medicine co-administration drugs with vinegar-baked Radix Bupleuri (VBRB) is usually used to increase the therapeutic effect in liver disease. However, the scientific data for this effect are not available. In this paper, effect of VBRB on the distribution of oxymatrine was studied. Mice were divided into four groups by random, oxymatrine control and oxymatrine co-administered with three different doses of VBRB. Concentrations of oxymatrine and its metabolite matrine in different tissues were determined by HPLC-MS. Target effencicy was evalutaed by AUC, Cmax, and Relative targeting efficiency (RTE). The results showed that compared to the control group, VBRB significantly increased the distribution of both oxymatrine and matrine in liver and meanwhile decreased their distribution in other tissues, indicating a strong liver targeting enhancing effect. The results of this paper implied that co-administration with VBRB may be a simple and efficiencient method for liver targeting therapy.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"31 1","pages":"740-745"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83456520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
U. Erdogdu, Mehmet Tan, R. Alhajj, Faruk Polat, D. Demetrick, J. Rokne
For certain domains, e.g. bioinformatics, producing more real samples is costly, error prone and time consuming. Therefore, there is a need for an intelligent automated process capable of substituting the real samples by artificial samples that carry the same characteristics as the real samples and hence could be used for running comprehensive testing of new methodologies. Motivated by this need, we describe a novel approach that integrates Probabilistic Boolean Network and genetic algorithm based techniques into a framework that uses some existing real samples as input and successfully produces new samples as output. The new samples will inspire the characteristics of the existing samples without duplicating them. This leads to diversity in the samples and hence a more rich set of samples to be used in testing. The developed framework incorporates two models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a high demanding area that has not received attention. The two perspectives employed in the process are based on models that are not closely related, the independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.
{"title":"Employing Machine Learning Techniques for Data Enrichment: Increasing the Number of Samples for Effective Gene Expression Data Analysis","authors":"U. Erdogdu, Mehmet Tan, R. Alhajj, Faruk Polat, D. Demetrick, J. Rokne","doi":"10.1109/BIBM.2011.105","DOIUrl":"https://doi.org/10.1109/BIBM.2011.105","url":null,"abstract":"For certain domains, e.g. bioinformatics, producing more real samples is costly, error prone and time consuming. Therefore, there is a need for an intelligent automated process capable of substituting the real samples by artificial samples that carry the same characteristics as the real samples and hence could be used for running comprehensive testing of new methodologies. Motivated by this need, we describe a novel approach that integrates Probabilistic Boolean Network and genetic algorithm based techniques into a framework that uses some existing real samples as input and successfully produces new samples as output. The new samples will inspire the characteristics of the existing samples without duplicating them. This leads to diversity in the samples and hence a more rich set of samples to be used in testing. The developed framework incorporates two models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a high demanding area that has not received attention. The two perspectives employed in the process are based on models that are not closely related, the independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"9 1","pages":"238-242"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88615678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112563
D. Snider, Xiaowei Xu
The fetal magnetocardiogram (fMCG) contains a wealth of information regarding the health of a fetus. The purpose of this study is to classify fMCG data into the following two groups: high-risk and normal. In this presentation the authors first describe how the feature vector containing both time and frequency domain attributes is built from the time-series fMCG data. Second, the classification process using support vector machine (SVM) tools to identify the high-risk fetuses is described. Experimental results from 272 data sets taken from 118 fetuses demonstrate the SVM classifier's ability to distinguish between the high-risk and normal fetuses. Artificial neural networks and decision trees are used to validate the SVM results and receiver operating characteristic curve analysis and blind tests are employed to show the strength of the model. The model currently attains a sensitivity of 0.67 and a specificity of 0.65. While this study remains a work in progress, the authors are refining the process to improve the aforementioned results.
{"title":"Mining fetal magnetocardiogram data for high-risk fetuses","authors":"D. Snider, Xiaowei Xu","doi":"10.1109/BIBMW.2011.6112563","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112563","url":null,"abstract":"The fetal magnetocardiogram (fMCG) contains a wealth of information regarding the health of a fetus. The purpose of this study is to classify fMCG data into the following two groups: high-risk and normal. In this presentation the authors first describe how the feature vector containing both time and frequency domain attributes is built from the time-series fMCG data. Second, the classification process using support vector machine (SVM) tools to identify the high-risk fetuses is described. Experimental results from 272 data sets taken from 118 fetuses demonstrate the SVM classifier's ability to distinguish between the high-risk and normal fetuses. Artificial neural networks and decision trees are used to validate the SVM results and receiver operating characteristic curve analysis and blind tests are employed to show the strength of the model. The model currently attains a sensitivity of 0.67 and a specificity of 0.65. While this study remains a work in progress, the authors are refining the process to improve the aforementioned results.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"9 1","pages":"1066-1068"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84797373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112437
Jie Wu, Pavani Davuluri, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, R. Hobson, K. Najarian
Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.
{"title":"Fracture detection and quantitative measure of displacement in pelvic CT images","authors":"Jie Wu, Pavani Davuluri, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, R. Hobson, K. Najarian","doi":"10.1109/BIBMW.2011.6112437","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112437","url":null,"abstract":"Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"14 1","pages":"600-606"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86702180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: Traditional Chinese Medicine (TCM) focuses on individual diagnosis. Besides the analysis methods on group level, clinical experimental data could also be researched with Information Technology to optimize the feature for individual healing effect; Method: we propose and apply a new method of feature optimization — Balance-Bagging-PRFS — to optimize the feature of insomnia intervened by TCM, aiming at solving problems typically in TCM data, such as mixing of discrete and continuous features and data imbalance; Result: from the view of all data, it is found that different levels of "ISI baseline score" and "Insomnia severity" have important influence on the curative effect. In treat group, different values of "environment" and "social field baseline" make remarkable difference on curative effect; while in control group, in which patients are treated with the placebo, "social field baseline", "survival quality baseline", and "classification of constitution" make sense; Conclusion: the method of Balance-Bagging-PRFS achieves good results in feature optimization for data from insomnia interfered by TCM, and it provides a basis for TCM individual diagnosis and for further optimization of symptom.
{"title":"Balance-bagging-PRFS algorithm for feature optimization on insomnia data intervened by traditional Chinese Medicine","authors":"Xiao-bo Yang, Shixing Yan, Zheng-yang Zhou, Guozheng Li, Yan Li, Xin-feng Guo","doi":"10.1109/BIBMW.2011.6112485","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112485","url":null,"abstract":"Goal: Traditional Chinese Medicine (TCM) focuses on individual diagnosis. Besides the analysis methods on group level, clinical experimental data could also be researched with Information Technology to optimize the feature for individual healing effect; Method: we propose and apply a new method of feature optimization — Balance-Bagging-PRFS — to optimize the feature of insomnia intervened by TCM, aiming at solving problems typically in TCM data, such as mixing of discrete and continuous features and data imbalance; Result: from the view of all data, it is found that different levels of \"ISI baseline score\" and \"Insomnia severity\" have important influence on the curative effect. In treat group, different values of \"environment\" and \"social field baseline\" make remarkable difference on curative effect; while in control group, in which patients are treated with the placebo, \"social field baseline\", \"survival quality baseline\", and \"classification of constitution\" make sense; Conclusion: the method of Balance-Bagging-PRFS achieves good results in feature optimization for data from insomnia interfered by TCM, and it provides a basis for TCM individual diagnosis and for further optimization of symptom.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"28 1","pages":"854-857"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82017161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes and functional modules, which is crucial to understanding the principles of cellular organization and prediction of protein functions. In the past few years, many computational methods have been proposed. However, it is always a challenging task to evaluate how well the clusters are identified. Even for the most popular measurements, F-measure and Pvalue, bias exists for evaluating the identified clusters. In this paper, we propose a new measurement, named hF-measure, to evaluate clusters more finely and distinctly. First, we defined the hierarchical consistency and the hierarchical similarity. Then, we propose a new hierarchical measurement of hF-measure by taking into account the hierarchical organization of functional annotations and the functional similarities among proteins. The new measurement hF-measure can discriminate between different types of errors which cannot be distinguished by F-measure. The experimental results based on Gene Ontology (GO) and yeast functional modules show that hF-measure evaluates clusters more accurately when compared to F-measure.
{"title":"A New Measurement for Evaluating Clusters in Protein Interaction Networks","authors":"Min Li, Xuehong Wu, Jianxin Wang, Yi Pan","doi":"10.1109/BIBM.2011.47","DOIUrl":"https://doi.org/10.1109/BIBM.2011.47","url":null,"abstract":"Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes and functional modules, which is crucial to understanding the principles of cellular organization and prediction of protein functions. In the past few years, many computational methods have been proposed. However, it is always a challenging task to evaluate how well the clusters are identified. Even for the most popular measurements, F-measure and Pvalue, bias exists for evaluating the identified clusters. In this paper, we propose a new measurement, named hF-measure, to evaluate clusters more finely and distinctly. First, we defined the hierarchical consistency and the hierarchical similarity. Then, we propose a new hierarchical measurement of hF-measure by taking into account the hierarchical organization of functional annotations and the functional similarities among proteins. The new measurement hF-measure can discriminate between different types of errors which cannot be distinguished by F-measure. The experimental results based on Gene Ontology (GO) and yeast functional modules show that hF-measure evaluates clusters more accurately when compared to F-measure.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"8 1","pages":"63-68"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83621992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112556
Tin Chi Nguyen, N. Deng, Guorong Xu, Z. Duan, D. Zhu
Transcript quantification using RNA-seq is central to contemporary and future transcriptomics research. The existing tools are useful but have much room for improvement. We present a new statistical model, a fast yet accurate transcript quantification algorithm. Our tool takes RNA-seq reads in fasta or fastq format as input and output transcript abundance through a few mouse clicks. Our method compares favorably with the existing GUI tools in terms of both time complexity and accuracy. Availability: Both simulation data used for method comparisons and the GUI tool are freely available at http://asammate.sourceforge.net/.
{"title":"iQuant: A fast yet accurate GUI tool for transcript quantification","authors":"Tin Chi Nguyen, N. Deng, Guorong Xu, Z. Duan, D. Zhu","doi":"10.1109/BIBMW.2011.6112556","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112556","url":null,"abstract":"Transcript quantification using RNA-seq is central to contemporary and future transcriptomics research. The existing tools are useful but have much room for improvement. We present a new statistical model, a fast yet accurate transcript quantification algorithm. Our tool takes RNA-seq reads in fasta or fastq format as input and output transcript abundance through a few mouse clicks. Our method compares favorably with the existing GUI tools in terms of both time complexity and accuracy. Availability: Both simulation data used for method comparisons and the GUI tool are freely available at http://asammate.sourceforge.net/.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"119 1","pages":"1048-1050"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91121161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sohee Oh, Jaehoon Lee, Min-Seok Kwon, Kyunga Kim, T. Park
Most common complex traits are affected by multiple genes and/or environmental factors. To understand genetic architecture of complex traits, the investigation of gene-gene and gene-environment interactions can be essential. However, conducting gene-gene interaction using genome-wide data requires exploring a huge search space and suffers from a computation burden due to high dimensionality of genetic data. To identify gene-gene interaction more efficiently, we propose a gene-based reduction method which first summarizes the gene effect by combining multiple single nucleotide polymorphism (SNP) and then performs the gene-gene interaction via the summarized gene effect. By reducing the search space from SNPs to gene, our gene-based method becomes efficient and fast for identifying gene-gene interaction in genome wide association studies. The gene-based reduction method is illustrated by hypertension data from a Korean population.
{"title":"Efficient and Fast Analysis for Detecting High Order Gene-by-Gene Interactions in a Genome-Wide Association Study","authors":"Sohee Oh, Jaehoon Lee, Min-Seok Kwon, Kyunga Kim, T. Park","doi":"10.1109/BIBM.2011.103","DOIUrl":"https://doi.org/10.1109/BIBM.2011.103","url":null,"abstract":"Most common complex traits are affected by multiple genes and/or environmental factors. To understand genetic architecture of complex traits, the investigation of gene-gene and gene-environment interactions can be essential. However, conducting gene-gene interaction using genome-wide data requires exploring a huge search space and suffers from a computation burden due to high dimensionality of genetic data. To identify gene-gene interaction more efficiently, we propose a gene-based reduction method which first summarizes the gene effect by combining multiple single nucleotide polymorphism (SNP) and then performs the gene-gene interaction via the summarized gene effect. By reducing the search space from SNPs to gene, our gene-based method becomes efficient and fast for identifying gene-gene interaction in genome wide association studies. The gene-based reduction method is illustrated by hypertension data from a Korean population.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"49 1","pages":"83-88"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76874598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metabolite identification is the major bottle-neck in LC-MS based metabolomic investigations. The mass-based search approach often leaves a large fraction of metabolites with either no identification or multiple putative identifications. As manual verification of metabolites is laborious, computational approaches are needed to obtain more reliable putative identifications and prioritize them. In this paper, a computational pipeline is proposed to assist metabolite identification with improved coverage and prioritization capability. The pipeline is based on multiple pieces of publicly-available software and databases. The proposed pipeline is successfully applied in an LC-MS/MS-based metabolomic study, where mass, retention time, and MS/MS spectrum were used to improve the accuracy of metabolite identification and to prioritize putative identifications for subsequent metabolite verification.
{"title":"A Computational Pipeline for LC-MS/MS Based Metabolite Identification","authors":"Bin Zhou, J. Xiao, H. Ressom","doi":"10.1109/BIBM.2011.89","DOIUrl":"https://doi.org/10.1109/BIBM.2011.89","url":null,"abstract":"Metabolite identification is the major bottle-neck in LC-MS based metabolomic investigations. The mass-based search approach often leaves a large fraction of metabolites with either no identification or multiple putative identifications. As manual verification of metabolites is laborious, computational approaches are needed to obtain more reliable putative identifications and prioritize them. In this paper, a computational pipeline is proposed to assist metabolite identification with improved coverage and prioritization capability. The pipeline is based on multiple pieces of publicly-available software and databases. The proposed pipeline is successfully applied in an LC-MS/MS-based metabolomic study, where mass, retention time, and MS/MS spectrum were used to improve the accuracy of metabolite identification and to prioritize putative identifications for subsequent metabolite verification.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"70 1","pages":"247-251"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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