Conference Proceeding

A new hybrid method for Bayesian network learning With dependency constraints

Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC
05/2009; DOI:10.1109/CIDM.2009.4938629 pp.53 - 60 In proceeding of: Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Source: IEEE Xplore

ABSTRACT A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.

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    Article: Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis.
    Jens Keilwagen, Jan Grau, Stefan Posch, Ivo Grosse
    [show abstract] [hide abstract]
    ABSTRACT: One of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or silencers, translation initiation sites, transcription start sites, transcription factor binding sites, nucleosome binding sites, miRNA binding sites, or insulator binding sites. During the last decade, a wealth of algorithms for the recognition of such DNA sequences has been developed and compared with the goal of improving their performance and to deepen our understanding of the underlying cellular processes. Most of these algorithms are based on statistical models belonging to the family of Markov random fields such as position weight matrix models, weight array matrix models, Markov models of higher order, or moral Bayesian networks. While in many comparative studies different learning principles or different statistical models have been compared, the influence of choosing different prior distributions for the model parameters when using different learning principles has been overlooked, and possibly lead to questionable conclusions. With the goal of allowing direct comparisons of different learning principles for models from the family of Markov random fields based on the same a-priori information, we derive a generalization of the commonly-used product-Dirichlet prior. We find that the derived prior behaves like a Gaussian prior close to the maximum and like a Laplace prior in the far tails. In two case studies, we illustrate the utility of the derived prior for a direct comparison of different learning principles with different models for the recognition of binding sites of the transcription factor Sp1 and human donor splice sites. We find that comparisons of different learning principles using the same a-priori information can lead to conclusions different from those of previous studies in which the effect resulting from different priors has been neglected. We implement the derived prior is implemented in the open-source library Jstacs to enable an easy application to comparative studies of different learning principles in the field of sequence analysis.
    BMC Bioinformatics 03/2010; 11:149. · 2.75 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

adapt local search algorithms
 
additional dependency information
 
Bayes net
 
Bayes net structures
 
Bayes nets
 
BDeu/BIC scores
 
conditional dependencies
 
conditional independencies
 
data fit
 
GES search
 
graphical structure
 
hybrid criterion
 
model selection criteria
 
net parameters
 
qualitative aspect
 
quantitative aspects
 
sample d
 
Simulation studies
 
statistical tests
 
structures G