11 research outputs found
Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks
Modeling biological networks serves as both a major goal and an effective
tool of systems biology in studying mechanisms that orchestrate the activities
of gene products in cells. Biological networks are context specific and dynamic
in nature. To systematically characterize the selectively activated regulatory
components and mechanisms, the modeling tools must be able to effectively
distinguish significant rewiring from random background fluctuations. We
formulated the inference of differential dependency networks that incorporates
both conditional data and prior knowledge as a convex optimization problem, and
developed an efficient learning algorithm to jointly infer the conserved
biological network and the significant rewiring across different conditions. We
used a novel sampling scheme to estimate the expected error rate due to random
knowledge and based on which, developed a strategy that fully exploits the
benefit of this data-knowledge integrated approach. We demonstrated and
validated the principle and performance of our method using synthetic datasets.
We then applied our method to yeast cell line and breast cancer microarray data
and obtained biologically plausible results.Comment: 7 pages, 7 figure
Fusión de redes Bayesianas Gaussianas
Las redes Bayesianas constituyen un modelo ampliamente utilizado para la representación
de relaciones de dependencia condicional en datos multivariantes. Su aprendizaje a partir de
un conjunto de datos o expertos ha sido estudiado profundamente desde su concepción. Sin
embargo, en determinados escenarios se demanda la obtención de un modelo común asociado
a particiones de datos o conjuntos de expertos. En este caso, se trata el problema de fusión
o agregación de modelos. Los trabajos y resultados en agregación de redes Bayesianas son
de naturaleza variada, aunque escasos en comparación con aquellos de aprendizaje. En este
documento, se proponen dos métodos para la agregación de redes Gaussianas, definidas como
aquellas redes Bayesianas que modelan una distribución Gaussiana multivariante. Los métodos
presentados son efectivos, precisos y producen redes con menor cantidad de parámetros en
comparación con los modelos obtenidos individualmente. Además, constituyen un enfoque
novedoso al incorporar nociones exploradas tradicionalmente por separado en el estado del arte.
Futuras aplicaciones en entornos escalables hacen dichos métodos especialmente atractivos,
dada su simplicidad y la ganancia en compacidad de la representación obtenida.---ABSTRACT---Bayesian networks are a widely used model for the representation of conditional dependence
relationships among variables in multivariate data. The task of learning them from a data set
or experts has been deeply studied since their conception. However, situations emerge where
there is a need of obtaining a consensuated model from several data partitions or a set of
experts. This situation is referred to as model fusion or aggregation. Results about Bayesian
network aggregation, although rich in variety, have been scarce when compared to the learning
task. In this context, two methods are proposed for the aggregation of Gaussian Bayesian
networks, that is, Bayesian networks whose underlying modelled distribution is a multivariate
Gaussian. Both methods are effective, precise and produce networks with fewer parameters in
comparison with the models obtained by individual learning. They constitute a novel approach
given that they incorporate notions traditionally explored separately in the state of the art.
Future applications in scalable computer environments make such models specially attractive,
given their simplicity and the gaining in sparsity of the produced model