Assessing the Effectiveness of Causality Inference Methods for Gene Regulatory Networks

Ahmed, Syed Sazzad; Roy, Swarup; Kalita, Jugal

doi:10.1109/tcbb.2018.2853728

Cited by 10 publications

(5 citation statements)

References 55 publications

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“…However, it remains a challenge to learn causality directly from genomic data. It is even harder to learn (i.e., infer) a causal network of multiple genes, which may represent which genes regulate which other genes (Hill et al, 2016; Ahmed et al, 2018). We address this problem in this paper.…”

Section: Introductionmentioning

confidence: 99%

Learning Causal Biological Networks With the Principle of Mendelian Randomization

Badsha

2019

Front. Genet.

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Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i. e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when multiple phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in the PC algorithm, a classical algorithm for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating individual-level genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms several popular general-purpose network inference methods and PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci. Our method is implemented in the R package MRPC, available on CRAN ( https://cran.r-project.org/web/packages/MRPC/index.html ).

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Section: Introductionmentioning

confidence: 99%

Learning Causal Biological Networks With the Principle of Mendelian Randomization

Badsha

2019

Front. Genet.

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“…The method used for causal relation inferencing here is the Markov Blankets (MB) method and Bayesian Network (BN) learning [ 31 , 32 , 33 , 34 ]. Joint conditional probabilities are represented by a graph in a Bayesian network, the nodes (genes) are connected by the Markov property which states that a node is conditionally independent of its nondescendants, given its parents.…”

Section: Methodsmentioning

confidence: 99%

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight

Manian

Orozco-Sandoval

Díaz-Martínez

2021

Genes

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Ionizing radiation present in extraterrestrial environment is an important factor that affects plants grown in spaceflight. Pearson correlation-based gene regulatory network inferencing from transcriptional responses of the plant Arabidopsis thaliana L. grown in real and simulated spaceflight conditions acquired by GeneLab, followed by topological and spectral analysis of the networks is performed. Gene regulatory subnetworks are extracted for DNA damage response processes. Analysis of radiation-induced ATR/ATM protein–protein interactions in Arabidopsis reveals interaction profile similarities under low radiation doses suggesting novel mechanisms of DNA damage response involving non-radiation-induced genes regulating other stress responses in spaceflight. The Jaccard similarity index shows that the genes AT2G31320, AT4G21070, AT2G46610, and AT3G27060 perform similar functions under low doses of radiation. The incremental association Markov blanket method reveals non-radiation-induced genes linking DNA damage response to root growth and plant development. Eighteen radiation-induced genes and sixteen non-radiation-induced gene players have been identified from the ATR/ATM protein interaction complexes involved in heat, salt, water, osmotic stress responses, and plant organogenesis. Network analysis and logistic regression ranking detected AT3G27060, AT1G07500, AT5G66140, and AT3G21280 as key gene players involved in DNA repair processes. High atomic weight, high energy, and gamma photon radiation result in higher intensity of DNA damage response in the plant resulting in elevated values for several network measures such as spectral gap and girth. Nineteen flavonoid and carotenoid pigment activations involved in pigment biosynthesis processes are identified in low radiation dose total light spaceflight environment but are not found to have significant regulations under very high radiation dose environment.

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“…The method used for causal relation inferencing used here is the Markov Blankets (MB) method and Bayesian Network (BN) learning ( Tsamardinos et al, 2003 ; Ram and Chetty, 2011 ; Syed Sazzad et al, 2020 ). Joint conditional probabilities are represented by a graph in a Bayesian network, the nodes (genes) are connected by Markov property which states that a node is conditionally independent of its non-descendants, given its parents.…”

Section: Methodsmentioning

confidence: 99%

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

Manian

Orozco-Sandoval

Díaz-Martínez

2021

Front. Cell Dev. Biol.

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Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein–protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene–disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene–disease and disease–drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene–disease, and disease–drug networks.

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Assessing the Effectiveness of Causality Inference Methods for Gene Regulatory Networks

Cited by 10 publications

References 55 publications

Learning Causal Biological Networks With the Principle of Mendelian Randomization

Learning Causal Biological Networks With the Principle of Mendelian Randomization

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

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