Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Causality draws the relationship between the cause and the effect among a pair of items. Causality inference in Gene Regulatory Networks (GRNs) is a challenging task due to the limits of appropriate data and large dataset size of real networks. Such an inference is very important because it might give the blueprint of how a complex organism operates which might further reveal the probable cause of some of the terminal diseases. A number of techniques have been proposed and applied to infer causal relationships in various domains. However, they are not specific to regulatory network inference. In our work, we made an empirical study on a few selective methods in inferring causal GRNs. To assess the candidate methods in terms of their quality of inference we used time-series expression data from the DREAM challenges. Our findings revealed that among the selected causality finding techniques, Causation Entropy gives the best result, however, it is computationally expensive. We used the second-best approach, Transfer Entropy, to infer casual GRN of breast cancer datasets. The topological analysis of the network revealed that top out-degree genes such as SLC39A5 which are considered central genes, plays an important role in cancer progression. The main intention behind this research work was to develop an effective inference method for causal Gene Regulatory Network which is computationally less expensive and also considers the effects of time-delay during regulation of one gene by the other. We proposed a novel time delayed inferring method for GRNs using association rule mining. We tried to infer the regulatory sign of the edges i.e. whether influencing positively or negatively. Comparative results highlight that our proposed technique produced better inference results in comparison to several of the existing techniques. We applied the proposed method to perform post inference analysis on malignant cancer network and benign network. Various networks parameters like the degree distribution, average path length, clustering coefficient, network re-wiring structure analysis, network topological variation analysis, and connectivity variation analysis are performed to identify the differentially expressed genes between the two networks. To avoid inference biasness and to obtain a consensus based conclusion we made a comparative analysis between our proposed method and transfer entropy based method and also computed the average score. Such an analysis of the network has revealed four genes, namely, STON 2 , UMODL 1 , STK 11 , and COBL which are already under investigation for its role in various types of cancer as seen from various researches.
Causality draws the relationship between the cause and the effect among a pair of items. Causality inference in Gene Regulatory Networks (GRNs) is a challenging task due to the limits of appropriate data and large dataset size of real networks. Such an inference is very important because it might give the blueprint of how a complex organism operates which might further reveal the probable cause of some of the terminal diseases. A number of techniques have been proposed and applied to infer causal relationships in various domains. However, they are not specific to regulatory network inference. In our work, we made an empirical study on a few selective methods in inferring causal GRNs. To assess the candidate methods in terms of their quality of inference we used time-series expression data from the DREAM challenges. Our findings revealed that among the selected causality finding techniques, Causation Entropy gives the best result, however, it is computationally expensive. We used the second-best approach, Transfer Entropy, to infer casual GRN of breast cancer datasets. The topological analysis of the network revealed that top out-degree genes such as SLC39A5 which are considered central genes, plays an important role in cancer progression. The main intention behind this research work was to develop an effective inference method for causal Gene Regulatory Network which is computationally less expensive and also considers the effects of time-delay during regulation of one gene by the other. We proposed a novel time delayed inferring method for GRNs using association rule mining. We tried to infer the regulatory sign of the edges i.e. whether influencing positively or negatively. Comparative results highlight that our proposed technique produced better inference results in comparison to several of the existing techniques. We applied the proposed method to perform post inference analysis on malignant cancer network and benign network. Various networks parameters like the degree distribution, average path length, clustering coefficient, network re-wiring structure analysis, network topological variation analysis, and connectivity variation analysis are performed to identify the differentially expressed genes between the two networks. To avoid inference biasness and to obtain a consensus based conclusion we made a comparative analysis between our proposed method and transfer entropy based method and also computed the average score. Such an analysis of the network has revealed four genes, namely, STON 2 , UMODL 1 , STK 11 , and COBL which are already under investigation for its role in various types of cancer as seen from various researches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.