SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks.

摘要: The establishment of a collaborative network transcription factors (TFs) followed by decomposition and then construction subnetworks is an effective way to obtain sets TFs; each set controls biological process or complex trait. We previously developed eight gene association methods for genome-wide coexpression analysis between TF all other genomic genes then constructing networks TFs but only one algorithm, called Triple-Link Algorithm, building subnetworks. In this study, we two more algorithms, Single Seed-Growing Algorithm (SSGA) Multi-Seed Growing (MSGA), identifying the fully-linked triple-node seeds from decomposed growing them into with different strategies. built three algorithms described above were comparatively appraised in terms both functional cohesion intra-subnetwork strengths versus inter-subnetwork strengths. concluded that SSGA MSGA, which performed systemic comparisons analyses edge weights connectivity during subnetwork processes, yielded cohesive than Triple-Link Algorithm. Together, these provide alternate approaches acquiring TFs. also presented framework outline how use these obtain collaborative governing processes traits.