Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method

Aghdam, Rosa; Alijanpour, Mohsen; Azadi, Mehrdad; Ebrahimi, Ali; Eslahchi, Changiz; Rezvan, Abolfazl

doi:10.1142/s1793524516500406

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…The Path Consistency (PC) method and its improvements (PC-based methods) are used for inferring the structure of GRN. 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 .…”

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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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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“…The Path Consistency Algorithm (PCA) is commonly used as a constraint-based method to infer GRNs. 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].…”

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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“…By deciphering these complex networks of immune cells, proteins and other molecules in steady state and disease, new targets for immunotherapy that refine or combat the state of immune activation or immune suppression, respectively, in tumor microenvironment will emerge [4][5][6]. Hence, the more network concept in biological systems is taken into account, the less assessing immune elements individually is considered effective in developing a reliable treatment [7][8][9][10]. Anyway, irrefutable help of mathematics in the form of Graph theory by defining a graph of Vertices (nodes) and Edges, can depict complex biological networks with numerous components and their interactions [11].…”

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confidence: 99%

Using structural controllability in detecting effective immune system elements against cancer cells

Ebrahimi,

Fayazi,

Ghavami

et al. 2024

Preprint

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Motivation: Cancer, a leading cause of death worldwide, is intertwined with immune system. Changing the state of immune system, as a complex network of cells and molecules interacting in a hierarchical mode, holds considerable fascination to fight cancer. This alteration can involve debilitating pro-tumorigenic and reinvigorating anti-tumorigenic functions by the help of driver vertices. The utilization of network controllability and control centrality-based analysis will aid in the identification of driver vertices to control target proteins that play significant roles in the anti-tumorigenic and pro-tumorigenic functions of immune cells. In this study, a weighted generalization of the control centrality was constructed, in which the control power of each vertex in the network is obtained based on the importance of a set of under control vertices. Results: Screening all homo sapiens-related pathways in KEGG database, we find that “Immune and cancer pathways” subnetwork showed the greatest number of edges and nodes in relation to positive and negative target proteins. The first five driver vertices obtained for positive targets are ICAM2, TLR2, MAP2K4, ELK1, SIRT1 and for negative targets are ELK4, RBX1, NFATC1, NFATC2, NFATC3. Our results suggest that exposing the external signal to reinvigorate driver vertices of positive targets and debilitate driver vertices of negative target will result in changing the immune system state to fight cancer.

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Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method

Cited by 5 publications

References 18 publications

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

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

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

Using structural controllability in detecting effective immune system elements against cancer cells

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