Inferring gene regulatory network using path consistency algorithm based on conditional mutual information and genetic algorithm
The interactions between genes can be described in the form of an intrinsic and interwoven network called Gene Regulatory Network. Discovering this interaction and accurate modeling of Gene Regulatory Network is one of the key issues in understanding the fundamental cell processes which may be used...
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| Veröffentlicht in: | 2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) S. 98 - 103 |
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01.10.2017
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| Abstract | The interactions between genes can be described in the form of an intrinsic and interwoven network called Gene Regulatory Network. Discovering this interaction and accurate modeling of Gene Regulatory Network is one of the key issues in understanding the fundamental cell processes which may be used in various medical, complex genetic diseases and drug discovery applications. In this paper, a method for inferring the gene regulatory network using a combination of Genetic Algorithm and Path Consistency Algorithm based on Conditional Mutual information is presented. In this method, for each gene, a genetic algorithm is utilized to find the most suitable predictor set of that gene. Moreover, in order to reduce the search space, the initial population for each target gene is created using the predictors obtained from Path Consistency Algorithm based on Conditional Mutual information method. To guide Genetic Algorithm, the multiple Pearson correlation coefficient is used. The obtained results using three evaluation criteria for biological data show that the proposed model performs better than recent similar methods. |
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| AbstractList | The interactions between genes can be described in the form of an intrinsic and interwoven network called Gene Regulatory Network. Discovering this interaction and accurate modeling of Gene Regulatory Network is one of the key issues in understanding the fundamental cell processes which may be used in various medical, complex genetic diseases and drug discovery applications. In this paper, a method for inferring the gene regulatory network using a combination of Genetic Algorithm and Path Consistency Algorithm based on Conditional Mutual information is presented. In this method, for each gene, a genetic algorithm is utilized to find the most suitable predictor set of that gene. Moreover, in order to reduce the search space, the initial population for each target gene is created using the predictors obtained from Path Consistency Algorithm based on Conditional Mutual information method. To guide Genetic Algorithm, the multiple Pearson correlation coefficient is used. The obtained results using three evaluation criteria for biological data show that the proposed model performs better than recent similar methods. |
| Author | Iranmanesh, Sima Ghavami, Behnam Sattari-Naeini, Vahid |
| Author_xml | – sequence: 1 givenname: Sima surname: Iranmanesh fullname: Iranmanesh, Sima email: sima_iranmanesh@yahoo.com organization: Dept. of Comput. Eng., Shahid Bahonar Univ. of Kerman, Kerman, Iran – sequence: 2 givenname: Vahid surname: Sattari-Naeini fullname: Sattari-Naeini, Vahid email: vsnaeini@uk.ac.ir organization: Dept. of Comput. Eng., Shahid Bahonar Univ. of Kerman, Kerman, Iran – sequence: 3 givenname: Behnam surname: Ghavami fullname: Ghavami, Behnam email: ghavami@uk.ac.ir organization: Dept. of Comput. Eng., Shahid Bahonar Univ. of Kerman, Kerman, Iran |
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| Snippet | The interactions between genes can be described in the form of an intrinsic and interwoven network called Gene Regulatory Network. Discovering this interaction... |
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| SubjectTerms | Biological cells Correlation Correlation coefficient Gene expression genetic algorithm Genetic algorithms Genetic regulatory network path consistency algorithm based on conditional mutual information Pearson correlation coefficient predictor subset Sociology |
| Title | Inferring gene regulatory network using path consistency algorithm based on conditional mutual information and genetic algorithm |
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