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

Algorithm

Description

Application and findings

BK

Bron-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].

IPCA

A 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].

CPM

Clique 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].

SA

A 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].