Using Networks to Integrate Omic and Semantic Data: Towards Understanding
Protein Function on a Genome-scale
My talk will be concerned with topics in proteomics, in particular
predicting protein function on a genomic scale. We approach this
through the prediction and analysis of biological networks, focusing
on protein-protein interaction and transcription-factor-target ones. I
will describe how these networks can be determined through integration
of many genomic features (including those derived from using the semantic web
and text mining) and how they can be analyzed in terms of
various simple topological statistics. In particular, I will discuss: (1)
Integrating gene expression data with
the regulatory network illuminates transient hubs; (2) Integration of
the protein interaction network with 3D molecular structures reveals
different types of hubs, depending on the number of interfaces
involved in interactions (one or many); (3) Analysis of betweenness in
biological networks reveals that this quantity is more strongly
correlated with essentially than degree; (4) Analysis of structure of
the regulatory network shows that it has a hierarchiel layout with the
"middle-managers" acting as information bottlenecks; (5) Development
of a useful web-based tools for the analysis of networks, PubNet and
tYNA; (6) Using known semantic web relationships as training sets to
improve biological query applications. And (7) using literature data to predict
Integrated prediction of the helical membrane protein interactome in
yeast. Y Xia, LJ Lu, M Gerstein (2006) J Mol Biol 357: 339-49.
Relating three-dimensional structures to protein networks provides
evolutionary insights. PM Kim, LJ Lu, Y Xia, MB Gerstein (2006)
Science 314: 1938-41.
The tYNA platform for comparative interactomics: a web tool for
managing, comparing and mining multiple networks. KY Yip, H Yu, PM
Kim, M Schultz, M Gerstein (2006) Bioinformatics 22: 2968-70.
The importance of bottlenecks in protein networks: correlation with
gene essentiality and expression dynamics. H Yu, PM Kim, E Sprecher,
V Trifonov, M Gerstein (2007) PLoS Comput Biol 3: e59.
Genomic analysis of the hierarchical structure of regulatory networks.
H Yu, M Gerstein (2006) Proc Natl Acad Sci U S A 103: 14724-31.
The role of disorder in interaction networks: a structural analysis.
PM Kim, A Sboner, Y Xia, M Gerstein (2008) Mol Syst Biol 4: 179.
Positive selection at the protein network periphery: evaluation in terms of
structural constraints and cellular context.
PM Kim, JO Korbel, MB Gerstein (2007) Proc Natl Acad Sci U S A 104: 20274-9.
Leveraging the structure of the Semantic Web to enhance information retrieval
A Smith, K Cheung, M Krauthammer, M Schultz, M Gerstein (2007) Bioinformatics
LinkHub: a Semantic Web system that facilitates cross-database queries and
information retrieval in proteomics.
AK Smith, KH Cheung, KY Yip, M Schultz, MK Gerstein (2007) BMC Bioinformatics 8
Suppl 3: S5.
Data mining on the web.
A Smith, M Gerstein (2006) Science 314: 1682;