19 research outputs found
Protein-Interaction-Networks: More than mere modules
Cellular function is widely believed to be organized in a modular fashion. On
all scales and at all levels of complexity, relatively independent sub-units
perform relatively independent sub-tasks of biological function. This
functional modularity must be reflected in the topology of molecular networks.
But how a functional module should be represented in an interaction network is
an open question. In protein-interaction networks (PIN), one can identify a
protein-complex as a module on a small scale, i.e. modules are understood as
densely linked, resp. interacting, groups of proteins, that are only sparsely
interacting with the rest of the network.
In this contribution, we show that extrapolating this concept of cohesively
linked clusters of proteins as modules to the scale of the entire PIN
inevitable misses important and functionally relevant structure inherent in the
network. As an alternative, we introduce a novel way of decomposing a network
into functional roles and show that this represents network structure and
function more efficiently. This finding should have a profound impact on all
module assisted methods of protein function prediction and should shed new
light on how functional modules can be represented in molecular interaction
networks in general
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Networks are a general language for representing relational information among
objects. An effective way to model, reason about, and summarize networks, is to
discover sets of nodes with common connectivity patterns. Such sets are
commonly referred to as network communities. Research on network community
detection has predominantly focused on identifying communities of densely
connected nodes in undirected networks.
In this paper we develop a novel overlapping community detection method that
scales to networks of millions of nodes and edges and advances research along
two dimensions: the connectivity structure of communities, and the use of edge
directedness for community detection. First, we extend traditional definitions
of network communities by building on the observation that nodes can be densely
interlinked in two different ways: In cohesive communities nodes link to each
other, while in 2-mode communities nodes link in a bipartite fashion, where
links predominate between the two partitions rather than inside them. Our
method successfully detects both 2-mode as well as cohesive communities, that
may also overlap or be hierarchically nested. Second, while most existing
community detection methods treat directed edges as though they were
undirected, our method accounts for edge directions and is able to identify
novel and meaningful community structures in both directed and undirected
networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1
Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme
<p>Abstract</p> <p>Background</p> <p>Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the <it>S. cerevisiae </it>interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.</p> <p>Results</p> <p>We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.</p> <p>Conclusions</p> <p>The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.</p
Generalized Blockmodeling of Multi-Valued Networks
This research presents an extension to generalized blockmodeling where there are more than two types of objects to be clustered based on valued network data. We use the ideas in homogeneity blockmodeling to develop an optimization model to perform the clustering of the objects and the resulting partitioning of the ties so as to minimize the inconsistency of an empirical block with an ideal block. The ideal block types used in this modeling are null (all zeros), complete (all ones) and valued. Two case studies are presented: the Southern Women dataset and a larger example using a subset of the IMDb movie dataset
BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks
Modern science of networks has brought significant advances to our understanding of complex systems biology. As a representative model of systems biology, Protein Interaction Networks (PINs) are characterized by a remarkable modular structures, reflecting functional associations between their components. Many methods were proposed to capture cohesive modules so that there is a higher density of edges within modules than those across them. Recent studies reveal that cohesively interacting modules of proteins is not a universal organizing principle in PINs, which has opened up new avenues for revisiting functional modules in PINs. In this paper, functional clusters in PINs are found to be able to form unorthodox structures defined as bi-sparse module. In contrast to the traditional cohesive module, the nodes in the bi-sparse module are sparsely connected internally and densely connected with other bi-sparse or cohesive modules. We present a novel protocol called the BinTree Seeking (BTS) for mining both bi-sparse and cohesive modules in PINs based on Edge Density of Module (EDM) and matrix theory. BTS detects modules by depicting links and nodes rather than nodes alone and its derivation procedure is totally performed on adjacency matrix of networks. The number of modules in a PIN can be automatically determined in the proposed BTS approach. BTS is tested on three real PINs and the results demonstrate that functional modules in PINs are not dominantly cohesive but can be sparse. BTS software and the supporting information are available at: www.csbio.sjtu.edu.cn/bioinf/BTS/
Comparative Study for Inference of Hidden Classes in Stochastic Block Models
Inference of hidden classes in stochastic block model is a classical problem
with important applications. Most commonly used methods for this problem
involve na\"{\i}ve mean field approaches or heuristic spectral methods.
Recently, belief propagation was proposed for this problem. In this
contribution we perform a comparative study between the three methods on
synthetically created networks. We show that belief propagation shows much
better performance when compared to na\"{\i}ve mean field and spectral
approaches. This applies to accuracy, computational efficiency and the tendency
to overfit the data.Comment: 8 pages, 5 figures AIGM1