A method is proposed, whereby a particular application of an operator, applied to a structure rep... more A method is proposed, whereby a particular application of an operator, applied to a structure representing a Bayesian network equivalence class can be scored in a generic fashion. This is achieved by representing a particular compound operator in terms of a finite set of primitive operators and finding the score of the compound operator through the influence of the primitive operators on the equivalence class. This method could be used in a Bayesian network structure learning framework which allows arbitrary definition of operators at runtime, by the composition of primitive operators.
Modelling gene regulatory networks in organisms is an important task that has recently become pos... more Modelling gene regulatory networks in organisms is an important task that has recently become possible due to large scale assays using technologies such as microarrays. In this paper, the circadian clock of Arabidopsis thaliana is modelled by fitting dynamic Bayesian networks to luminescence data gathered from experiments. This work differs from previous modelling attempts by using higher-order dynamic Bayesian networks to explicitly model the time lag between the various genes being expressed. In order to achieve this goal, new techniques in preprocessing the data and in evaluating a learned model are proposed. It is shown that it is possible, to some extent, to model these time delays using a higher-order dynamic Bayesian network.
Background: The importance of appropriate normalization controls in quantitative real-time polyme... more Background: The importance of appropriate normalization controls in quantitative real-time polymerase chain reaction (qPCR) experiments has become more apparent as the number of biological studies using this methodology has increased. In developing a system to study gene expression from transiently transfected plasmids, it became clear that normalization using chromosomally encoded genes is not ideal, at it does not take into account the transfection efficiency and the significantly lower expression levels of the plasmids. We have developed and validated a normalization method for qPCR using a co-transfected plasmid. Results: The best chromosomal gene for normalization in the presence of the transcriptional activators used in this study, cadmium, dexamethasone, forskolin and phorbol-12-myristate 13-acetate was first identified. qPCR data was analyzed using geNorm, Normfinder and BestKeeper. Each software application was found to rank the normalization controls differently with no clear correlation. Including a co-transfected plasmid encoding the Renilla luciferase gene (Rluc) in this analysis showed that its calculated stability was not as good as the optimised chromosomal genes, most likely as a result of the lower expression levels and transfection variability. Finally, we validated these analyses by testing two chromosomal genes (B2M and ActB) and a co-transfected gene (Rluc) under biological conditions. When analyzing co-transfected plasmids, Rluc normalization gave the smallest errors compared to the chromosomal reference genes.
Page 1. SPEEDING UP THE LEARNING OF EQUIVALENCE CLASSES OF BAYESIAN NETWORK STRUCTURES Rónán Daly... more Page 1. SPEEDING UP THE LEARNING OF EQUIVALENCE CLASSES OF BAYESIAN NETWORK STRUCTURES Rónán Daly School of Informatics University of Edinburgh Edinburgh EH8 9LE, UK [email protected] Qiang ...
Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper prop... more Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed on the ACO construction graph. Secondly, moves can be given in terms of arbitrary identifiers. The algorithm is implemented and tested. The results show that ACO performs better than a greedy search whilst searching in the space of equivalence classes.
Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper prop... more Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.
A method is proposed, whereby a particular application of an operator, applied to a structure rep... more A method is proposed, whereby a particular application of an operator, applied to a structure representing a Bayesian network equivalence class can be scored in a generic fashion. This is achieved by representing a particular compound operator in terms of a finite set of primitive operators and finding the score of the compound operator through the influence of the primitive operators on the equivalence class. This method could be used in a Bayesian network structure learning framework which allows arbitrary definition of operators at runtime, by the composition of primitive operators.
Modelling gene regulatory networks in organisms is an important task that has recently become pos... more Modelling gene regulatory networks in organisms is an important task that has recently become possible due to large scale assays using technologies such as microarrays. In this paper, the circadian clock of Arabidopsis thaliana is modelled by fitting dynamic Bayesian networks to luminescence data gathered from experiments. This work differs from previous modelling attempts by using higher-order dynamic Bayesian networks to explicitly model the time lag between the various genes being expressed. In order to achieve this goal, new techniques in preprocessing the data and in evaluating a learned model are proposed. It is shown that it is possible, to some extent, to model these time delays using a higher-order dynamic Bayesian network.
Background: The importance of appropriate normalization controls in quantitative real-time polyme... more Background: The importance of appropriate normalization controls in quantitative real-time polymerase chain reaction (qPCR) experiments has become more apparent as the number of biological studies using this methodology has increased. In developing a system to study gene expression from transiently transfected plasmids, it became clear that normalization using chromosomally encoded genes is not ideal, at it does not take into account the transfection efficiency and the significantly lower expression levels of the plasmids. We have developed and validated a normalization method for qPCR using a co-transfected plasmid. Results: The best chromosomal gene for normalization in the presence of the transcriptional activators used in this study, cadmium, dexamethasone, forskolin and phorbol-12-myristate 13-acetate was first identified. qPCR data was analyzed using geNorm, Normfinder and BestKeeper. Each software application was found to rank the normalization controls differently with no clear correlation. Including a co-transfected plasmid encoding the Renilla luciferase gene (Rluc) in this analysis showed that its calculated stability was not as good as the optimised chromosomal genes, most likely as a result of the lower expression levels and transfection variability. Finally, we validated these analyses by testing two chromosomal genes (B2M and ActB) and a co-transfected gene (Rluc) under biological conditions. When analyzing co-transfected plasmids, Rluc normalization gave the smallest errors compared to the chromosomal reference genes.
Page 1. SPEEDING UP THE LEARNING OF EQUIVALENCE CLASSES OF BAYESIAN NETWORK STRUCTURES Rónán Daly... more Page 1. SPEEDING UP THE LEARNING OF EQUIVALENCE CLASSES OF BAYESIAN NETWORK STRUCTURES Rónán Daly School of Informatics University of Edinburgh Edinburgh EH8 9LE, UK [email protected] Qiang ...
Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper prop... more Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed on the ACO construction graph. Secondly, moves can be given in terms of arbitrary identifiers. The algorithm is implemented and tested. The results show that ACO performs better than a greedy search whilst searching in the space of equivalence classes.
Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper prop... more Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.
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