CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test

Aghdam, Rosa; Ganjali, Mojtaba; Zhang, Xiujun; Eslahchi, Changiz

doi:10.1039/c4mb00413b

Cited by 34 publications

(49 citation statements)

References 36 publications

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“…Liu et al (2015) [ 56 ] take a Bayesian approach to add and prune edges to maximize the posterior likelihood of the data. Agdham et al (2015) [ 57 ] attacked the inference problem with an information-theoretic approach, which performs well in their benchmark but cannot infer edge directionality. Zhang et al (2014) [ 58 ] used a conditional mutual information measure to prune down from a fully connected graph by eliminating nodes with mutual information explained by intermediaries very much like the previously published ARACNe method [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Prophetic Granger Causality to infer gene regulatory networks

et al. 2017

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We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

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Section: Discussionmentioning

confidence: 99%

Prophetic Granger Causality to infer gene regulatory networks

et al. 2017

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“…The SORDER algorithm is proposed, selecting an appropriate sequential node ordering. To introduce the algorithm, some notations are used.…”

Section: Methodsmentioning

confidence: 99%

Some node ordering methods for the K2 algorithm

Aghdam

Tabar

Pezeshk

2018

Computational Intelligence

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Inferring Bayesian network structure from data is a challenging issue, and many researchers have been working on this problem. The K2 is a well‐known order‐dependent algorithm to learn Bayesian network. The result of the algorithm is not robust since it achieves different network structure if node orderings are permuted. Consequently, choosing suitable sequential node ordering for the input of the K2 algorithm is a challenging task. In this work, some deterministic methods for selecting a suitable sequential node ordering are introduced. The effectiveness of these methods benchmarked through the Asia, Alarm, Car, and Insurance networks. The results indicate that the methods based on the concept of mutual information and entropy are suitable for finding a sequential node ordering and considerably improves the precision of network inference. The source code and selected data sets are available on http://profiles.bs.ipm.ir/softwares/ordering/.

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“…The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Consensus Network (CN) [15], introduced Sequential ORDERing (SORDER) algorithm to selects a suitable sequential ordering of genes. It also improves the accuracy of the obtained results by taking the consensus of different networks.…”

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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CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test

Cited by 34 publications

References 36 publications

Prophetic Granger Causality to infer gene regulatory networks

Prophetic Granger Causality to infer gene regulatory networks

Some node ordering methods for the K2 algorithm

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

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