20 research outputs found

    Competitive algorithms for unbounded one-way trading

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    In the one-way trading problem, a seller has L units of product to be sold to a sequence σ of buyers u1,u2,…,uσu1,u2,…,uσ arriving online and he needs to decide, for each ui, the amount of product to be sold to ui at the then-prevailing market price pi. The objective is to maximize the seller's revenue. We note that all previous algorithms for the problem need to impose some artificial upper bound M and lower bound m on the market prices, and the seller needs to know either the values of M and m , or their ratio M/mM/m, at the outset....[cont'd

    PEP Search in MyCompoundID: Detection and Identification of Dipeptides and Tripeptides Using Dimethyl Labeling and Hydrophilic Interaction Liquid Chromatography Tandem Mass Spectrometry

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    Small peptides, such as dipeptides and tripeptides, are naturally present in many biological samples (e.g., human biofluids and cell extracts). They have attracted great attention in many research fields because of their important biological functions as well as potential roles as disease biomarkers. Tandem mass spectrometry (MS/MS) can be used to profile these small peptides. However, the type and number of fragment ions generated in MS/MS are often limited for unambiguous identification. Herein we report a novel database-search strategy based on the use of MS/MS spectra of both unlabeled and dimethyl labeled peptides to identify and confirm amino acid sequences of di/tripeptides that are separated using hydrophilic interaction (HILIC) liquid chromatography (LC). To facilitate the di/tripeptide identification, a database consisting of all the predicted MS/MS spectra from 400 dipeptides and 8000 tripeptides was created, and a search tool, PEP Search, was developed and housed at the MyCompoundID website (www.mycompoundid.org/PEP). To evaluate the identification specificity of this method, we used acid hydrolysis to degrade a standard protein, cytochrome c, to produce many di/tripeptides with known sequences for LC/MS/MS. The resultant MS/MS spectra were searched against the database to generate a list of matches which were compared to the known sequences. We correctly identified the di/tripeptides in the protein hydrolysate. We then applied this method to detect and identify di/tripeptides naturally present in human urine samples with high confidence. We envisage the use of this method as a complementary tool to various LC/MS techniques currently available for small molecule or metabolome profiling with an added benefit of covering all di/tripeptide chemical space

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-6

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    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p>Carcinomas dataset

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-4

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    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p>with the F-test and GS methods, on the Carcinomas dataset

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-1

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    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p>Carcinomas dataset

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-3

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    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p> are selected using (·, ·) and (·,·) distances combined with the F-test method, on the Carcinomas dataset

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-2

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    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p>or the first run of the -means gene clustering algorithm plotted together with the average classification accuracies and the standard deviations over 100 runs of the -means algorithm

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-7

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    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p>or the first run of the -means gene clustering algorithm plotted together with the average classification accuracies and the standard deviations over 100 runs of the -means algorithm

    Selecting dissimilar genes for multi-class classification, an application in cancer subtyping-5

    No full text
    <p><b>Copyright information:</b></p><p>Taken from "Selecting dissimilar genes for multi-class classification, an application in cancer subtyping"</p><p>http://www.biomedcentral.com/1471-2105/8/206</p><p>BMC Bioinformatics 2007;8():206-206.</p><p>Published online 16 Jun 2007</p><p>PMCID:PMC1914361.</p><p></p>uster, on the Carcinomas dataset

    DnsID in MyCompoundID for Rapid Identification of Dansylated Amine- and Phenol-Containing Metabolites in LC–MS-Based Metabolomics

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    High-performance chemical isotope labeling (CIL) liquid chromatography–mass spectrometry (LC–MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC–MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC–MS
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