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Brian Saunders

dblp:80/335 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › biological database
pathway database
0.112011
Signaling gateway molecule pages - a data model perspective · Bioinform. 2011
Bioinformatics and computational biology
data integration
0.012011
Signaling gateway molecule pages - a data model perspective · Bioinform. 2011

Methods — techniques the papers use, named apart from their topics

data modeling · 0.1XML export · 0.1
YearPublicationVenuePosition
2011 Signaling gateway molecule pages - a data model perspective
abstract
SUMMARY: The Signaling Gateway Molecule Pages (SGMP) database provides highly structured data on proteins which exist in different functional states participating in signal transduction pathways. A molecule page starts with astate of a native protein, without any modification and/or interactions. New states are formed with every post-translational modification or interaction with one or more proteins, small molecules or class molecules and with each change in cellular location. State transitions are caused by a combination of one or more modifications, interactions and translocations which then might be associated with one or more biological processes. In a characterized biological state, a molecule can function as one of several entities or their combinations, including channel, receptor, enzyme, transcription factor and transporter. We have also exported SGMP data to the Biological Pathway Exchange (BioPAX) and Systems Biology Markup Language (SBML) as well as in our custom XML. AVAILABILITY: SGMP is available at www.signaling-gateway.org/molecule.
Ashok Reddy Dinasarapu, Brian Saunders, Iley Ozerlat, Kenan Azam, Shankar Subramaniam
Bioinform.2
2006 Components of the antigen processing and presentation pathway revealed by gene expression microarray analysis following B cell antigen receptor (BCR) stimulation
abstract
BACKGROUND: Activation of naïve B lymphocytes by extracellular ligands, e.g. antigen, lipopolysaccharide (LPS) and CD40 ligand, induces a combination of common and ligand-specific phenotypic changes through complex signal transduction pathways. For example, although all three of these ligands induce proliferation, only stimulation through the B cell antigen receptor (BCR) induces apoptosis in resting splenic B cells. In order to define the common and unique biological responses to ligand stimulation, we compared the gene expression changes induced in normal primary B cells by a panel of ligands using cDNA microarrays and a statistical approach, CLASSIFI (Cluster Assignment for Biological Inference), which identifies significant co-clustering of genes with similar Gene Ontology annotation. RESULTS: CLASSIFI analysis revealed an overrepresentation of genes involved in ion and vesicle transport, including multiple components of the proton pump, in the BCR-specific gene cluster, suggesting that activation of antigen processing and presentation pathways is a major biological response to antigen receptor stimulation. Proton pump components that were not included in the initial microarray data set were also upregulated in response to BCR stimulation in follow up experiments. MHC Class II expression was found to be maintained specifically in response to BCR stimulation. Furthermore, ligand-specific internalization of the BCR, a first step in B cell antigen processing and presentation, was demonstrated. CONCLUSION: These observations provide experimental validation of the computational approach implemented in CLASSIFI, demonstrating that CLASSIFI-based gene expression cluster analysis is an effective data mining tool to identify biological processes that correlate with the experimental conditional variables. Furthermore, this analysis has identified at least thirty-eight candidate components of the B cell antigen processing and presentation pathway and sets the stage for future studies focused on a better understanding of the components involved in and unique to B cell antigen processing and presentation.
Jamie A. Lee, Robert S. Sinkovits, Dennis Mock, Eva L. Rab, Jennifer Cai, Brian Saunders, Robert C. Hsueh, Sangdun Choi, Shankar Subramaniam, Richard H. Scheuermann
BMC Bioinform.7