Learning directed acyclic graphs by determination of candidate causes for discrete variables

Tabar, Vahid Rezaei; Zareifard, Hamid; Salimi, Selva; Plewczynski, Dariusz

doi:10.1080/00949655.2019.1604709

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In GRN, genes are denoted as nodes while the goal is to detect interactions between them, referred to as edges. Computational methods developed to reconstruct GRNs are generally categorized in either machine-learning-based or model-based methods 8 – 35 . In literature, Pearson correlation coefficients 36 , 37 and information theory 5 , 16 , 19 – 27 , 29 – 32 are widely used to measure the regulation strength between genes.…”

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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