45 research outputs found
CytoITMprobe: a network information flow plugin for Cytoscape
To provide the Cytoscape users the possibility of integrating ITM Probe into
their workflows, we developed CytoITMprobe, a new Cytoscape plugin.
CytoITMprobe maintains all the desirable features of ITM Probe and adds
additional flexibility not achievable through its web service version. It
provides access to ITM Probe either through a web server or locally. The input,
consisting of a Cytoscape network, together with the desired origins and/or
destinations of information and a dissipation coefficient, is specified through
a query form. The results are shown as a subnetwork of significant nodes and
several summary tables. Users can control the composition and appearance of the
subnetwork and interchange their ITM Probe results with other software tools
through tab-delimited files.
The main strength of CytoITMprobe is its flexibility. It allows the user to
specify as input any Cytoscape network, rather than being restricted to the
pre-compiled protein-protein interaction networks available through the ITM
Probe web service. Users may supply their own edge weights and
directionalities. Consequently, as opposed to ITM Probe web service,
CytoITMprobe can be applied to many other domains of network-based research
beyond protein-networks. It also enables seamless integration of ITM Probe
results with other Cytoscape plugins having complementary functionality for
data analysis.Comment: 16 pages, 6 figures. Version
Information Flow in Interaction Networks
Interaction networks, consisting of agents linked by their interactions, are
ubiquitous across many disciplines of modern science. Many methods of analysis
of interaction networks have been proposed, mainly concentrating on node degree
distribution or aiming to discover clusters of agents that are very strongly
connected between themselves. These methods are principally based on
graph-theory or machine learning.
We present a mathematically simple formalism for modelling context-specific
information propagation in interaction networks based on random walks. The
context is provided by selection of sources and destinations of information and
by use of potential functions that direct the flow towards the destinations. We
also use the concept of dissipation to model the aging of information as it
diffuses from its source.
Using examples from yeast protein-protein interaction networks and some of
the histone acetyltransferases involved in control of transcription, we
demonstrate the utility of the concepts and the mathematical constructs
introduced in this paper.Comment: 30 pages, 5 figures. This paper was published in 2007 in Journal of
Computational Biology. The version posted here does not include post
peer-review change
Information flow in interaction networks II: channels, path lengths and potentials
In our previous publication, a framework for information flow in interaction
networks based on random walks with damping was formulated with two fundamental
modes: emitting and absorbing. While many other network analysis methods based
on random walks or equivalent notions have been developed before and after our
earlier work, one can show that they can all be mapped to one of the two modes.
In addition to these two fundamental modes, a major strength of our earlier
formalism was its accommodation of context-specific directed information flow
that yielded plausible and meaningful biological interpretation of protein
functions and pathways. However, the directed flow from origins to destinations
was induced via a potential function that was heuristic. Here, with a
theoretically sound approach called the channel mode, we extend our earlier
work for directed information flow. This is achieved by constructing a
potential function facilitating a purely probabilistic interpretation of the
channel mode. For each network node, the channel mode combines the solutions of
emitting and absorbing modes in the same context, producing what we call a
channel tensor. The entries of the channel tensor at each node can be
interpreted as the amount of flow passing through that node from an origin to a
destination. Similarly to our earlier model, the channel mode encompasses
damping as a free parameter that controls the locality of information flow.
Through examples involving the yeast pheromone response pathway, we illustrate
the versatility and stability of our new framework.Comment: Minor changes from v3. 30 pages, 7 figures. Plain LaTeX format. This
version contains some additional material compared to the journal submission:
two figures, one appendix and a few paragraph
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer
Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks