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DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research

DiffGRN: differential gene regulatory network analysis
Int J Data Min Bioinform. 2018;20(4):362-379. doi: 10.1504/IJDMB.2018.094891.ABSTRACTIdentification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementa...
Source: International Journal of Data Mining and Bioinformatics - May 23, 2019 Category: Bioinformatics Authors: Youngsoon Kim Jie Hao Yadu Gautam Tesfaye B Mersha Mingon Kang Source Type: research