Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data

Broom, Bradley M.; Subramanian, Devika

doi:10.1017/cbo9780511584589.021

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Three major types of approaches have been developed for estimating the structures: (1) a score-and-search approach through the structure space, (2) a constraint-based approach that tests conditional independence identified in the data, and (3) a hybrid approach that combines both the score-and-search approach and the constraint-based approach. The score-and-search approach (Broom and Subramanian, 2006; Buntine, 1994, 1996; Heckerman et al, 1995; Neapolitan, 2004) searches for a DAG by maximizing a score function, which often consists of a model fitting part and a penalty of model complexity (Daly et al, 2011). Heuristic or ad hoc search algorithms are often employed to search for a high-scoring DAG without enumerating all possible structures.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

Han

Chen²,

Cheon

et al. 2016

Journal of the American Statistical Association

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Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed graphical models, where all the edges are directed edges and contain no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acyclic constraints, and the presence of equivalence class from observational data. To overcome these challenges, we propose a two-stage adaptive Lasso approach, called NS-DIST, which performs neighborhood selection (NS) in stage 1, and then estimates DAGs by the Discrete Improving Search with Tabu (DIST) algorithm within the selected neighborhood. Simulation studies are presented to demonstrate the effectiveness of the method and its computational efficiency. Two real data examples are used to demonstrate the practical usage of our method for gene regulatory network inference.

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Section: Introductionmentioning

confidence: 99%

Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

Han

Chen²,

Cheon

et al. 2016

Journal of the American Statistical Association

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show abstract

“…Regarding the implementation of BAIS, we notice that the algorithm does not require a large amount of computational resources [2]. Although a Bayesian network has to be produced at every p = 10 iterations, the proposed methodology still preserves the computational tractability due to the restriction of at most two parents for each node in the network.…”

Section: Comparative Resultsmentioning

confidence: 99%

Evaluating the performance of a Bayesian Artificial Immune System for designing fuzzy rule bases

Castro

Camargo

Zuben

2013

HIS

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In this paper we perform a deep investigation about the usefulness of an immune-inspired algorithm to design accurate and compact fuzzy rule bases for classification problems. The algorithm, called Bayesian Artificial Immune System (BAIS), incorporates a mechanism to learn a probability graphical model from the promising solutions found so far. Thus, BAIS utilizes this model to sample new candidate solutions. The probabilistic model utilized here is a Bayesian network due to its capability of expressing the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions (building blocks). Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and can be considered useful attributes when generating fuzzy rule bases, thus guiding to high-performance classifiers. BAIS was evaluated in thirteen well-known classification problems and its performance compares favorably with that produced by contenders.

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“…Regarding the implementation of BAIS, we notice that the algorithm does not require a large amount of computational resources [1]. Although a Bayesian network has to be produced at every p = 10 iterations, the proposed methodology still preserves the computational tractability due to the restriction of at most two parents for each node in the network.…”

Section: Resultsmentioning

confidence: 99%

Designing fuzzy rule bases with a Bayesian Artificial Immune System

Castro

Camargo

Zuben

2011

2011 11th International Conference on Hybrid Intelligent Systems (HIS)

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In this paper we apply an immune-inspired approach to generate fuzzy rule bases for classification problems. Our proposal, called Bayesian Artificial Immune System (BAIS), is a hybrid algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Thus, the algorithm takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained highquality partial solutions (building blocks). Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and can be considered useful attributes when generating fuzzy rule bases, thus guiding to high-performance classifiers. BAIS was evaluated in six wellknown classification problems and its performance compares favorably with that produced by contenders.

show abstract

Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data

Cited by 6 publications

References 26 publications

Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

Evaluating the performance of a Bayesian Artificial Immune System for designing fuzzy rule bases

Designing fuzzy rule bases with a Bayesian Artificial Immune System

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