Bayesian Network Learning Algorithm Research Articles

LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.

Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

Using Consensus Bayesian Network to Model the Reactive Oxygen Species Regulatory Pathway

Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the Bayesian network from microarray data directly. Although large numbers of Bayesian network learning algorithms have been developed, when applying them to learn Bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn Bayesian networks contain too few microarray data. In this paper, we propose a consensus Bayesian network which is constructed by combining Bayesian networks from relevant literatures and Bayesian networks learned from microarray data. It would have a higher accuracy than the Bayesian networks learned from one database. In the experiment, we validated the Bayesian network combination algorithm on several classic machine learning databases and used the consensus Bayesian network to model the 's ROS pathway.

An artificial bee colony algorithm for learning Bayesian networks

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic Algorithm, Evolutionary Programming and Ant Colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.

One iteration CHC algorithm for learning Bayesian networks: an effective and efficient algorithm for high dimensional problems

It is well known that learning Bayesian networks from data is an NP-hard problem. For this reason, usually metaheuristics or approximate algorithms have been used to provide a good solution. In particular, the family of hill climbing algorithms has a key role in this scenario because of its good trade-off between computational demand and the quality of the learned models. In addition, these algorithms have several good theoretical properties. In spite of these characteristics of quality and efficiency, when it comes to dealing with high-dimensional datasets, they can be improved upon, and this is the goal of this paper. Recent papers have tackled this problem, usually by dividing the learning task into two or more iterations or phases. The first phase aims to constrain the search space, and, once the space is pruned, the second one consists of a (local) search in this constrained search space. Normally, the first iteration is the one with the highest computational complexity. One such algorithm is constrained hill climbing (CHC), which in its initial iteration not only progressively constrains the search space, but also learns good quality Bayesian networks. A second iteration, or even more, is used in order to improve these networks and also to ensure the good theoretical properties exhibited by the classical hill climbing algorithm. In this latter algorithm we can see that the first iteration is extremely fast when compared to similar algorithms, but the performance decays over the rest of the iterations with respect to the saved CPU time. In this paper, we present an improvement on this CHC algorithm, in which, to put it, briefly, we avoid the last iteration while still obtaining the same theoretical properties. Furthermore, we experimentally test the proposed algorithms over a set of different domains, some of them quite large (more than 1,000 variables), in order to study their behavior in practice.

Scaling up the Greedy Equivalence Search algorithm by constraining the search space of equivalence classes

Greedy Equivalence Search (GES) is nowadays the state of the art algorithm for learning Bayesian networks (BNs) from complete data. However, from a practical point of view, this algorithm may not be efficient enough to deal with data from high dimensionality and/or complex domains. This paper proposes some modifications to GES aimed at increasing its efficiency. Under the faithfulness assumption, the modified algorithms preserve the same theoretical properties of the original one, that is, they recover a perfect map of the target distribution in the large sample limit. Moreover, experimental results confirm that, although the proposed methods carry out a significantly smaller number of computations, the quality of the BNs learned can be compared with those obtained with GES.

Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization

Structure learning of Bayesian networks is a well-researched but computationally hard task. For learning Bayesian networks, this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization (U-ACO-B) to solve the drawbacks of the ant colony optimization (ACO-B). In this algorithm, firstly, an unconstrained optimization problem is solved to obtain an undirected skeleton, and then the ACO algorithm is used to orientate the edges, thus returning the final structure. In the experimental part of the paper, we compare the performance of the proposed algorithm with ACO-B algorithm. The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.

Learning Causal Relations in Multivariate Time Series Data

Many applications naturally involve time series data and the vector autoregression (VAR), and the structural VAR (SVAR) are dominant tools to investigate relations between variables in time series. In the first part of this work, we show that the SVAR method is incapable of identifying contemporaneous causal relations for Gaussian process. In addition, least squares estimators become unreliable when the scales of the problems are large and observations are limited. In the remaining part, we propose an approach to apply Bayesian network learning algorithms to identify SVARs from time series data in order to capture both temporal and contemporaneous causal relations, and avoid high-order statistical tests. The difficulty of applying Bayesian network learning algorithms to time series is that the sizes of the networks corresponding to time series tend to be large, and high-order statistical tests are required by Bayesian network learning algorithms in this case. To overcome the difficulty, we show that the search space of conditioning sets d-separating two vertices should be a subset of the Markov blankets. Based on this fact, we propose an algorithm enabling us to learn Bayesian networks locally, and make the largest order of statistical tests independent of the scales of the problems. Empirical results show that our algorithm outperforms existing methods in terms of both efficiency and accuracy.

A system for predicting and preventing work-related musculoskeletal disorders among dentists

Work-related musculoskeletal disorders (WMSDs) have become increasingly common among dentists and initiate a series of events that could result in a career ending. This study aims to construct a system for predicting and preventing WMSD among dentists. We used Bayesian network (BN) that describes the mutual relationships among multiple variables contributing to WMSDs. The data-sets were prepared from direct measurements of dentist's movements and a questionnaire survey. We applied BN learning algorithms to the training data-sets to develop WMSD prediction model using 10-fold cross-validation. To evaluate the system performance, 16 dentists were randomly assigned into a 2 × 2 crossover trial scheduled to each of two sequences of dental working: receiving feedback or no feedback including the probability of WMSD and related risk factors from the system. The group that received feedback decreased significantly (t-test, p < 0.05) the extensions of neck and upper back in the y-axis as well as the WMSD probability on the post-test. In conclusion, the system for predicting and preventing WMSD aids the correction of neck and upper back extensions and reduction in WMSD probability, which may potentially contribute to reduce the risk of WMSD among dentists.

A Decomposition Algorithm for Learning Bayesian Networks Based on Scoring Function

Learning Bayesian network (BN) structure from data is a typical NP‐hard problem. But almost existing algorithms have the very high complexity when the number of variables is large. In order to solve this problem(s), we present an algorithm that integrates with a decomposition‐based approach and a scoring‐function‐based approach for learning BN structures. Firstly, the proposed algorithm decomposes the moral graph of BN into its maximal prime subgraphs. Then it orientates the local edges in each subgraph by the K2‐scoring greedy searching. The last step is combining directed subgraphs to obtain final BN structure. The theoretical and experimental results show that our algorithm can efficiently and accurately identify complex network structures from small data set.

Mathematical modeling for active and dynamic diagnosis of crop diseases based on Bayesian networks and incremental learning

To achieve rapid and precise diagnosis of crop diseases, an active and dynamic method of diagnosis of crop diseases is needed and such a method is proposed in this paper. This method adopts Bayesian networks to represent the relationships among the symptoms and crop diseases. This method has two main differences from the existing diagnosis methods. First, it does not use all the symptoms in the diagnosis, but purposively selects a subset of symptoms which are the most relevant to diagnosis; the active symptom selection is based on the concept of a Markov blanket in a Bayesian network. Second, a specific incremental learning algorithm for Bayesian networks is also proposed to make the diagnosis model update dynamically over time in order to adapt to temporal changes of environment. Furthermore, the diagnosis results can be calculated without inference in Bayesian networks, so the method has low time complexity. Theoretical analysis and experimental results demonstrate that the proposed method can significantly enhance the performance of crop disease diagnosis.

Constrained Ant Colony Optimization B algorithm for Learning Bayesian Networks

Constrained Ant Colony Optimization B algorithm for Learning Bayesian Networks

3D object retrieval based on a graph model descriptor

A new 3D object retrieval approach is proposed based on a novel graph model descriptor and a fast graph matching method. Our methodology is made up of two steps. Firstly, a Bayesian network lightfield descriptor (BLD) is built, based on graph model learning, to overcome the disadvantages of the existing view-based methods. The 3D object is put into the lightfield, multi-view images are obtained; then features of the new multi-view images are extracted. Based on the extracted features, a Bayesian network learning algorithm is used to construct the BLD. Secondly, the 3D object is efficiently retrieved, based on graph model matching and learning from relevant feedback. Experimental results demonstrate that our algorithm has better performance and efficiency than the existing view-based methods.

Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study

This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Glymour and Fast Incremental Association Markov Blanket proposed by Yaramakala and Margaritis), a score-based algorithm (Hill Climbing as implemented by Daly), and a hybrid algorithm (Max-Min Hill Climbing proposed by Tsamardinos et al.). Contrary to the belief regarding the superiority of constraint-based approaches, our empirical results show that a score-based approach performs better on our context dataset in terms of prediction power and learning time. The hybrid algorithm could achieve similar prediction performance as the score-based approach, but requires longer time to learn the desired network. Another interesting fact the study has revealed is the existence of strong correspondence between the linear correlation pattern within the dataset and the edges found in the learned networks.

Adaptive Bayesian Networks for quantitative risk assessment of foreign body injuries in children

Injuries due to foreign body (FB) aspiration/ingestion/insertion represent a common public health issue in paediatric patients, which causes significant morbidity and mortality. The aim of this study is to present a Bayesian Network (BN) model for the identification of risk factors for FB injuries in children and provide their quantitative assessment. Combining a priori knowledge and observed data, a BN learning algorithm was used to generate the pattern of the relationships between possible causal factors of FB injuries. Finally, the BN was used for making inference on scenarios of interest, providing, for instance, the risk that an accident caused by a spherical object swallowed by a male child aged five while playing leads to hospitalization. BNs as a tool for quantitative risk assessment may assist in determining the hazard of consumer products giving an insight into their most influential specific features on the risk of experiencing severe injuries.

An Efficient Algorithm for Learning Bayesian Networks from Data

We propose an algorithm for learning an optimal Bayesian network from data. Our method is addressed to biological applications, where usually datasets are small, and sets of random variables are large. Moreover, we assume that there is no need to examine the acyclicity of the graph. We provide polynomial bounds (with respect to the number of random variables) for time complexity of our algorithm for two generally used scoring criteria: Minimal Description Length and Bayesian-Dirichlet equivalence. Then we show how to adapt these criteria to work with continuous data and prove polynomial bounds for adapted scores. Finally, we briefly describe applications of proposed algorithm in computational biology.

Towards a Method for Data Accuracy Assessment Utilizing a Bayesian Network Learning Algorithm

This research develops a data quality algorithm entitled the Accuracy Assessment Algorithm (AAA). This is an extension of research in developing an enhancement to a Bayesian Network (BN) learning algorithm called the Data Quality (DQ) algorithm. This new algorithm is concerned with estimating the accuracy levels of a dataset by assessing the quality of the data with no prior knowledge of the dataset. The AAA and associated metrics were tested using two canonical BNs and one large-scale medical network. The article presents the results regarding the efficacy of the algorithm and the implications for future research and practice.

A heuristic method for learning Bayesian networks using discrete particle swarm optimization

Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.

A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization

摘要: 针对ACO-B蚁群算法学习贝叶斯网结构的不足, 提出了一种基于独立性测试和蚁群优化的改进算法. 新算法首先利用0阶独立性测试来限制侯选结构的搜索空间, 避免了蚁群的一些不必要的搜索, 然后融合解的全局评分增益和节点间局部的互信息, 给出了启发能力更强的启发函数来引导随机搜索. 实验结果表明, 新算法能够更有效地处理大规模数据, 且学习速度有较大改进. 关键词: 不确定性知识表示和推理 / Bayesian网络结构学习 / 蚁群算法 / 条件独立性测试