11,883 research outputs found
Comparison of evolutionary algorithms in gene regulatory network model inference
Background: The evolution of high throughput technologies that measure gene expression levels has created a
data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of
these data has made this process very di±cult. At the moment, several methods of discovering qualitative
causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative
analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real
microarray data which are noisy and insu±cient.
Results: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene
regulatory network modelling. The aim is to present the techniques used and o®er a comprehensive comparison
of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression
data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.
Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene
regulatory networks. Promising methods are identi¯ed and a platform for development of appropriate model
formalisms is established
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated by the enormously large scale of the unknowns in a rather
small sample size. Furthermore, when the goal is to study causal relationships
within the network, tools capable of overcoming the limitations of correlation
networks are required. In this work, we make use of Bayesian Graphical Models
to attach this problem and, specifically, we perform a comparative study of
different state-of-the-art heuristics, analyzing their performance in inferring
the structure of the Bayesian Network from breast cancer data
Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions
Genetic regulatory networks (GRNs) have been widely studied, yet there is a
lack of understanding with regards to the final size and properties of these
networks, mainly due to no network currently being complete. In this study, we
analyzed the distribution of GRN structural properties across a large set of
distinct prokaryotic organisms and found a set of constrained characteristics
such as network density and number of regulators. Our results allowed us to
estimate the number of interactions that complete networks would have, a
valuable insight that could aid in the daunting task of network curation,
prediction, and validation. Using state-of-the-art statistical approaches, we
also provided new evidence to settle a previously stated controversy that
raised the possibility of complete biological networks being random and
therefore attributing the observed scale-free properties to an artifact
emerging from the sampling process during network discovery. Furthermore, we
identified a set of properties that enabled us to assess the consistency of the
connectivity distribution for various GRNs against different alternative
statistical distributions. Our results favor the hypothesis that highly
connected nodes (hubs) are not a consequence of network incompleteness.
Finally, an interaction coverage computed for the GRNs as a proxy for
completeness revealed that high-throughput based reconstructions of GRNs could
yield biased networks with a low average clustering coefficient, showing that
classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio
The Infinite Hierarchical Factor Regression Model
We propose a nonparametric Bayesian factor regression model that accounts for
uncertainty in the number of factors, and the relationship between factors. To
accomplish this, we propose a sparse variant of the Indian Buffet Process and
couple this with a hierarchical model over factors, based on Kingman's
coalescent. We apply this model to two problems (factor analysis and factor
regression) in gene-expression data analysis
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