Mining the Synergistic Core Allosteric Modules Variation and Sequencing Pharmacological Module Drivers in a Preclinical Model of Ischemia

Abstract

Identifying the variation of core modules and hubs seems to be critical for characterizing variable pharmacological mechanisms based on topological alteration of disease networks. We first identified a total of eight core modules by using an approach of multiple modular characteristic fusing (MMCF) from different targeted networks in ischemic mice. Interestingly, the value of module disturbance intensity (MDI) increased in drug combination group. Second, we redefined a weak allosteric module and a strong allosteric module. Then, we identified 15 pharmacological module drivers (PMDs) by leave‐one‐out screening with a cutoff of two folds, which were at least, in part, validated by expression and variation of topological contribution. Finally, we revealed the fusional and emergent variation of PMD in core modules contributing to multidimensional synergistic mechanism in ischemic mice and rats. Our findings provide a new set of drivers that might promote the pharmacological modular flexibility and offer a potential avenue for disease treatment.

RETRACTED: Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing

“…It is now possible to locate the HNS in the network, but it can be difficult to condense the main traits of HNS even though numerous exploratory investigations have been conducted. For instance, driver nodes (DNs) based on structural control theory [ 11 ] and network core module nodes (CMs) for measuring the significance of node sets are used to identify the important nodes in complex networks as drug targets [ 12 ]. The module nodes allow automated prediction of the function of unidentified protein complexes from high-quality protein–protein interaction data.…”

Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma

“…Three module-screening methods, connected components, the Markov cluster algorithm (MCL; parameters: number of iterations=16) 15 , and molecular complex detection (MCODE; parameters: degree cutoff=2, K-core=2, and node score threshold=0.2) 4 , were compared. The connected components are simple divisions based on connectivity 16 .…”

“…The MCL algorithm assigns genes into families based on precomputed sequence similarity information. MCODE is based on vertex weighting by local neighborhood density and outward traversal from locally dense seed genes to isolate dense regions according to the given parameters [4]. The network structure entropies of these regions are calculated to balance the selective speculation [17].…”

“…Core module genes were regarded as important, and the edges between these modules were derived from the intermolecular relations across modules. The approaches of multiple modular characteristic fusing (MMCF) [4], including degree, maximum neighborhood component (MNC), density of maximum neighborhood component (DMNC), maximal clique centrality (MCC), closeness (Clo), eccentricity (EC), radiality (Rad), bottleneck (BN), stress (Str), betweenness centrality (BC), and edge percolated component (EPC), were used for this search process [18][19][20][21][22][23]. After ranking the results of these 11 methods, the rank-sum ratio was used for comprehensive quantitative evaluation.…”

“…Researchers have conducted different exploratory studies regarding the importance of HNSs. For example, the structural properties of networks are mainly based on node centrality 2 , 3 , network core module nodes (CMs) for measuring the importance of node sets 4 and driver nodes (DNs) based on structural control theory 5 , where the aim is to identify the key nodes in complex networks as drug targets. Module nodes allow the automated prediction of protein complexes from qualitative protein-protein interaction data and are thus able to help predict the function of unknown proteins.…”