Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system
Autor: | Jongwoo Lim, Jaehoon Lim, Wang Bh |
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Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Neuro-fuzzy Gene regulatory network Saccharomyces cerevisiae Biology Bioinformatics computer.software_genre Fuzzy logic 03 medical and health sciences Fuzzy Logic Gene Expression Regulation Fungal Genetics Gene Regulatory Networks Sensitivity (control systems) Molecular Biology Selection (genetic algorithm) Models Genetic Artificial neural network Cell Cycle Computational Biology General Medicine Function (mathematics) Temporal database ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Neural Networks Computer ComputingMethodologies_GENERAL Data mining computer |
Zdroj: | Genetics and Molecular Research. 15 |
ISSN: | 1676-5680 |
Popis: | Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches. |
Databáze: | OpenAIRE |
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