Some node ordering methods for the K2 algorithm

Aghdam, Rosa; Tabar, Vahid Rezaei; Pezeshk, Hamid

doi:10.1111/coin.12182

Cited by 5 publications

(3 citation statements)

References 43 publications

(64 reference statements)

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“…Methods based on PCA such as Fast Causal Inference (FCI), Really Fast Causal Inference (RFCI), PC Algorithm based on Conditional Mutual Information (PCA-CMI) and their modifications [25,[40][41][42][43][44][45][46][47] have two common drawbacks. The first is that these methods are not consistent for different sequential node orders [48]. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing.…”

Section: Introductionmentioning

confidence: 99%

An Order Independent Algorithm for Inferring Gene Regulatory Network Using Quantile Value for Conditional Independent Tests

Mahmoodi

Aghdam

Eslahchi

2020

Preprint

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Background: In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring structure of Gene Regulatory Networks (GRNs). Bayesian network is one of the popular methods in this field, however, still has many drawbacks and there is still a great space to be improved. For example, the Path Consistency (PC)-based algorithms as Bayesian network methods are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. Results: We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different order of CMI test. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while others well-known PC-based methods only considers the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with others well-known PC-based methods. Conclusions: According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported by Zhang et al are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, and especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

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Section: Introductionmentioning

confidence: 99%

An Order Independent Algorithm for Inferring Gene Regulatory Network Using Quantile Value for Conditional Independent Tests

Mahmoodi

Aghdam

Eslahchi

2020

Preprint

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“…In the current era of big data, researchers pay more attention to learning BN structure from data. Existing algorithms for learning BN structures from data can generally be divided into three types: constraint-based (CB) [6,24,28,29], scorebased (SS) [10,30,33] and hybrid algorithms [3,12,15]. Among those algorithms, hybrid algorithms which combine merits of CB and SS algorithms show better performance.…”

Section: Introductionmentioning

confidence: 99%

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Guo

2022

Complex Intell. Syst.

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Decomposition hybrid algorithms with the recursive framework which recursively decompose the structural task into structural subtasks to reduce computational complexity are employed to learn Bayesian network (BN) structure. Merging rules are commonly adopted as the combination method in the combination step. The direction determination rule of merging rules has problems in using the idea of keeping v-structures unchanged before and after combination to determine directions of edges in the whole structure. It breaks down in one case due to appearances of wrong v-structures, and is hard to operate in practice. Therefore, we adopt a novel approach for direction determination and propose a two-stage combination method. In the first-stage combination method, we determine nodes, links of edges by merging rules and adopt the idea of permutation and combination to determine directions of contradictory edges. In the second-stage combination method, we restrict edges between nodes that do not satisfy the decomposition property and their parent nodes by determining the target domain according to the decomposition property. Simulation experiments on four networks show that the proposed algorithm can obtain BN structure with higher accuracy compared with other algorithms. Finally, the proposed algorithm is applied to the thickening process of gold hydrometallurgy to solve the practical problem.

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“…PC-based methods such as Fast Causal Inference (FCI), Really Fast Causal Inference (RFCI), PC Algorithm based on Conditional Mutual Information (PCA-CMI) and their modifications 25 , 39 – 46 have two common drawbacks. The first is that these methods are not consistent for different sequential node orders 47 . The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing.…”

Section: Introductionmentioning

confidence: 99%

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

Mahmoodi

Aghdam

Eslahchi

2021

Sci Rep

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In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

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Some node ordering methods for the K2 algorithm

Cited by 5 publications

References 43 publications

An Order Independent Algorithm for Inferring Gene Regulatory Network Using Quantile Value for Conditional Independent Tests

An Order Independent Algorithm for Inferring Gene Regulatory Network Using Quantile Value for Conditional Independent Tests

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

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