GAEM: Genetic Algorithm based Expectation-Maximization for inferring Gene Regulatory Networks from incomplete data
In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI e...
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| Published in: | Computers in biology and medicine Vol. 183; p. 109238 |
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01.12.2024
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| Abstract | In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM.
•GAEM learns GRN from incomplete data updating the imputed values using the GRN.•GAEM uses BNs to learn GRNs and applies GRNs’ information to impute missing values.•GAEM is not limited to a specific structure learning algorithm.•GAEM’s performance is notable in NMAR mechanisms when the network size is small. |
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| AbstractList | In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM.In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM. AbstractIn Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM. In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM. •GAEM learns GRN from incomplete data updating the imputed values using the GRN.•GAEM uses BNs to learn GRNs and applies GRNs’ information to impute missing values.•GAEM is not limited to a specific structure learning algorithm.•GAEM’s performance is notable in NMAR mechanisms when the network size is small. In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM. |
| ArticleNumber | 109238 |
| Author | Aghdam, Rosa Niloofar, Parisa Eslahchi, Changiz |
| Author_xml | – sequence: 1 givenname: Parisa orcidid: 0000-0002-3147-0078 surname: Niloofar fullname: Niloofar, Parisa email: parni@mmmi.sdu.dk organization: Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark – sequence: 2 givenname: Rosa surname: Aghdam fullname: Aghdam, Rosa organization: Wisconsin Institute for Discovery, University of Wisconsin-Madison, WI, Madison, USA – sequence: 3 givenname: Changiz surname: Eslahchi fullname: Eslahchi, Changiz organization: Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Iran |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39426072$$D View this record in MEDLINE/PubMed |
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| Keywords | Conditional Mutual Information Gene Regulatory Network Genetic algorithm Bayesian network Expectation-Maximization Missing values |
| Language | English |
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| SubjectTerms | Algorithms Bayesian network Bioinformatics Computational Biology - methods Conditional Mutual Information Convergence Databases, Genetic Datasets Expectation-Maximization Gene expression Gene Expression Profiling - methods Gene Regulatory Network Gene Regulatory Networks - genetics Genetic algorithm Genetic algorithms Humans Internal Medicine Machine learning Maximization Missing values Models, Genetic Optimization Other |
| Title | GAEM: Genetic Algorithm based Expectation-Maximization for inferring Gene Regulatory Networks from incomplete data |
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