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GetPubmed                          		prometheus.brc.mcw.edu
Retrieve PubMed articles in MEDLINE display format for given PMIDs
	
Result: <?xml version='1.0' encoding='UTF-8'?>
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            <Simple><moby:PubMed-MEDLINE namespace='PMID' id="9788350"><![CDATA[PMID- 9788350
OWN - NLM
STAT- completed
DA  - 19981228
DCOM- 19981228
LR  - 20011126
IS  - 1087-0156
VI  - 16
IP  - 10
DP  - 1998 Oct
TI  - Finding DNA regulatory motifs within unaligned noncoding sequences
      clustered by whole-genome mRNA quantitation.
PG  - 939-45
AB  - Whole-genome mRNA quantitation can be used to identify the genes that are
      most responsive to environmental or genotypic change. By searching for
      mutually similar DNA elements among the upstream non-coding DNA sequences
      of these genes, we can identify candidate regulatory motifs and
      corresponding candidate sets of coregulated genes. We have tested this
      strategy by applying it to three extensively studied regulatory systems in
      the yeast Saccharomyces cerevisiae: galactose response, heat shock, and
      mating type. Galactose-response data yielded the known binding site of
      Gal4, and six of nine genes known to be induced by galactose. Heat shock
      data yielded the cell-cycle activation motif, which is known to mediate
      cell-cycle dependent activation, and a set of genes coding for all four
      nucleosomal proteins. Mating type alpha and a data yielded all of the four
      relevant DNA motifs and most of the known a- and alpha-specific genes.
AD  - Harvard University Graduate Biophysics Program and Harvard Medical School
      Department of Genetics, Boston, MA 02115, USA.
FAU - Roth, F P
AU  - Roth FP
FAU - Hughes, J D
AU  - Hughes JD
FAU - Estep, P W
AU  - Estep PW
FAU - Church, G M
AU  - Church GM
LA  - eng
PT  - Journal Article
PL  - UNITED STATES
TA  - Nat Biotechnol
JID - 9604648
RN  - 0 (Peptides)
RN  - 0 (RNA, Messenger)
RN  - 26566-61-0 (Galactose)
RN  - 50-99-7 (Glucose)
RN  - 61194-02-3 (mating factor)
RN  - 9007-49-2 (DNA)
SB  - IM
CIN - Nat Biotechnol. 1998 Oct;16(10):907-8. PMID: 9788341
MH  - Cloning, Molecular
MH  - DNA/*genetics
MH  - Galactose/metabolism
MH  - Gene Expression Regulation, Fungal
MH  - Glucose/metabolism
MH  - Peptides/genetics
MH  - RNA, Messenger/*genetics
MH  - *Regulatory Sequences, Nucleic Acid
MH  - Saccharomyces cerevisiae/genetics/growth & development
MH  - Support, U.S. Gov't, Non-P.H.S.
EDAT- 1998/10/27
MHDA- 1998/10/27 00:01
PST - ppublish
SO  - Nat Biotechnol 1998 Oct;16(10):939-45.

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Result: <?xml version='1.0' encoding='UTF-8'?>
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        <moby:queryResponse>
            <Simple><moby:PubMed-MEDLINE namespace='PMID' id="14730315"><![CDATA[PMID- 14730315
OWN - NLM
STAT- in-process
DA  - 20040202
IS  - 1087-0156
VI  - 22
IP  - 2
DP  - 2004 Feb
TI  - Intensity-based protein identification by machine learning from a library
      of tandem mass spectra.
PG  - 214-9
AB  - Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of
      proteomics owing in part to robust spectral interpretation algorithms.
      Widely used algorithms do not fully exploit the intensity patterns present
      in mass spectra. Here, we demonstrate that intensity pattern modeling
      improves peptide and protein identification from MS/MS spectra. We modeled
      fragment ion intensities using a machine-learning approach that estimates
      the likelihood of observed intensities given peptide and fragment
      attributes. From 1,000,000 spectra, we chose 27,000 with high-quality,
      nonredundant matches as training data. Using the same 27,000 spectra,
      intensity was similarly modeled with mismatched peptides. We used these
      two probabilistic models to compute the relative likelihood of an observed
      spectrum given that a candidate peptide is matched or mismatched. We used
      a 'decoy' proteome approach to estimate incorrect match frequency, and
      demonstrated that an intensity-based method reduces peptide identification
      error by 50-96% without any loss in sensitivity.
FAU - Elias, Joshua E
AU  - Elias JE
FAU - Gibbons, Francis D
AU  - Gibbons FD
FAU - King, Oliver D
AU  - King OD
FAU - Roth, Frederick P
AU  - Roth FP
FAU - Gygi, Steven P
AU  - Gygi SP
LA  - eng
GR  - 5T32CA86878/CA/NCI
GR  - HG00041/HG/NHGRI
PT  - Letter
DEP - 20040118
PL  - United States
TA  - Nat Biotechnol
JID - 9604648
SB  - IM
EDAT- 2004/01/20 05:00
MHDA- 2004/01/20 05:00
PHST- 2003/Aug/11 [received]
PHST- 2003/Oct/31 [accepted]
PHST- 2004/Jan/18 [aheadofprint]
AID - 10.1038/nbt930 [doi]
AID - nbt930 [pii]
PST - ppublish
SO  - Nat Biotechnol 2004 Feb;22(2):214-9. Epub 2004 Jan 18.

]]></moby:PubMed-MEDLINE></Simple>
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Result: <?xml version='1.0' encoding='UTF-8'?>
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OWN - NLM
STAT- completed
DA  - 20021007
DCOM- 20030411
IS  - 1088-9051
VI  - 12
IP  - 10
DP  - 2002 Oct
TI  - Judging the quality of gene expression-based clustering methods using gene
      annotation.
PG  - 1574-81
AB  - We compare several commonly used expression-based gene clustering
      algorithms using a figure of merit based on the mutual information between
      cluster membership and known gene attributes. By studying various publicly
      available expression data sets we conclude that enrichment of clusters for
      biological function is, in general, highest at rather low cluster numbers.
      As a measure of dissimilarity between the expression patterns of two
      genes, no method outperforms Euclidean distance for ratio-based
      measurements, or Pearson distance for non-ratio-based measurements at the
      optimal choice of cluster number. We show the self-organized-map approach
      to be best for both measurement types at higher numbers of clusters.
      Clusters of genes derived from single- and average-linkage hierarchical
      clustering tend to produce worse-than-random results.
AD  - Department of Biological Chemistry and Molecular Pharmacology, Harvard
      Medical School, Boston, Massachusetts 02115, USA.
FAU - Gibbons, Francis D
AU  - Gibbons FD
FAU - Roth, Frederick P
AU  - Roth FP
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Genome Res
JID - 9518021
SB  - IM
MH  - Algorithms
MH  - *Cluster Analysis
MH  - Comparative Study
MH  - Computational Biology/*methods/statistics & numerical data
MH  - Data Interpretation, Statistical
MH  - Gene Expression Profiling/methods/*statistics & numerical data
MH  - Gene Expression Regulation, Fungal/genetics
MH  - *Quality Control
MH  - Saccharomyces cerevisiae/genetics
MH  - Support, Non-U.S. Gov't
EDAT- 2002/10/09 04:00
MHDA- 2003/04/12 05:00
AID - 10.1101/gr.397002 [doi]
PST - ppublish
SO  - Genome Res 2002 Oct;12(10):1574-81.

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