3,876 research outputs found
Multimodal Network Alignment
A multimodal network encodes relationships between the same set of nodes in
multiple settings, and network alignment is a powerful tool for transferring
information and insight between a pair of networks. We propose a method for
multimodal network alignment that computes a matrix which indicates the
alignment, but produces the result as a low-rank factorization directly. We
then propose new methods to compute approximate maximum weight matchings of
low-rank matrices to produce an alignment. We evaluate our approach by applying
it on synthetic networks and use it to de-anonymize a multimodal transportation
network.Comment: 14 pages, 6 figures, Siam Data Mining 201
Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach
Background
Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.
Results
Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.
Conclusions
The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection
We have developed an automatic method for segmenting fluorescence lifetime
(FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution
community detection (MCD) based network segmentation method. The image
processing problem is framed as identifying segments with respective average
FLTs against a background in FLIM images. The proposed method segments a FLIM
image for a given resolution of the network composed using image pixels as the
nodes and similarity between the pixels as the edges. In the resulting
segmentation, low network resolution leads to larger segments and high network
resolution leads to smaller segments. Further, the mean-square error (MSE) in
estimating the FLT segments in a FLIM image using the proposed method was found
to be consistently decreasing with increasing resolution of the corresponding
network. The proposed MCD method outperformed a popular spectral clustering
based method in performing FLIM image segmentation. The spectral segmentation
method introduced noisy segments in its output at high resolution. It was
unable to offer a consistent decrease in MSE with increasing resolution.Comment: 21 pages, 6 figure
Erratum: Signal propagation in proteins and relation to equilibrium fluctuations (PLoS Computational Biology (2007) 3, 9, (e172) DOI: 10.1371/journal.pcbi.0030172))
Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biological function. We posit that equilibrium motions also determine communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the number of steps it takes on an average to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topological basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have physical origins directly relevant to the equilibrium fluctuations of residues predicted by EN models
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
A temporal precedence based clustering method for gene expression microarray data
Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.
Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system.
Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits
Extending Tlusty's method to the glycome: Tuning the repertoire of glycan determinants
We apply Tlusty's information-theoretic analysis of the genetic code to the glycome, using a cognitive paradigm in which external information sources constrain and tune the glycan code error network, in the context of available metabolic energy. The resulting dynamic model suggests the possibility of observing spontaneous symmetry breaking of the glycan code as a function of metabolic energy intensity. These effects may be currently present, or embedded in evolutionary trajectory, recording large-scale ecosystem resilience shifts in energy availability such as the aerobic transition
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