More than hundred algorithms were developed to infer Gene Regulatory Networks (GRN) describing relations between genes. GRN construction has been a field of interest to researchers since the beginning of the current century. Many competitions were held to encourage the development of GRN inference algorithms, such competitions offer synthetic data to enable the validation of proposed algorithms. A GRN is constructed from an adjacency matrix which contains relations between genes. The developers of many of the GRN inference algorithms set a threshold on the adjacency matrix to construct GRN based on high gene-gene relation weights. This threshold strategy was followed in previous studies to increase the accuracy of any algorithm but yet based on no well-known rule. A different perspective here is to compare different GRN inference algorithms without setting any threshold. Comparison in this work is among different GRN inference algorithms by implementing all algorithms with no threshold on values of adjacency matrices: Differential Equation methods (TSNI), Granger Causality, GP4GRN, GENIE3, NIMEFI (SVR), and PLSNET. Another comparison between different distance metric equations to create adjacency matrix is also studied in an attempt to construct GRN. GP4GRN and GENIE3 participate in producing best results for dream4 InSilico_Size10 while GENIE3 produce best results for all networks of dream4 InSilico_Size100.
gene regulatory network, adjacency matrix, distance metrics
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