20 research outputs found
Competitive algorithms for unbounded one-way trading
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
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
<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
<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
<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
<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
<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
<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
<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
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