Table 3: Network-based subgroup analysis approaches in TCM.

AlgorithmDescriptionApplication and findings

BKBron-Kerbosch algorithm: an efficient algorithm for finding all maximal cliques of a network. The recursive procedure for optimizing candidate selection is performed based on the three different sets (R, P, X) of nodes, where R represents the currently growing clique (initially empty), P denotes prospective nodes, and X stands for the nodes already processed [69].Applied for the discovery of basic formula (BF) in herbal prescriptions of the famous TCM expert. Three BFs for psoriasis and four BFs for eczema were found [58].

K-core A subnetwork detecting methods to find the required clusters in which all the nodes have at least k degree [64].Applied for the subnetworks analysis of TCM ingredients target-target network, as well as for the measuring centrality of nodes by “ value” [77].
Applied for clustering symptoms for differentiating TCM syndrome of coronary heart disease based on the symptom-symptom network [76].

IPCAA network-based clustering algorithm to identify subgroups based on the new topological structure [170]. Applied for clustering functional proteins of PPIs network based on TCM cold and hot syndromes [80] or TCM therapy [123].

CPMClique percolation Method for finding such a subgroup that corresponds to fully connected k nodes [56].Applied for detecting synergistic or antagonistic subgroups of clinical factors networks in TCM tumor treatment [78].

SAA simulated annealing algorithm, which is a generic probabilistic metaheuristic of the global optimizing for decomposing the networks [73]. Applied for subgroups detecting based on pathway-pathway association network for salvianolic acid B [79].