RedNemo: topology-based PPI network reconstruction via repeated diffusion with neighborhood modifications

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

Characterization of a non-coding RNA-associated ceRNA network in metastatic lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly malignant cancer. Although competing endogenous RNA (ceRNA)-based profiling has been investigated in patients with LUAD, it has not been specifically used to study metastasis in LUAD. We found 130 differentially expressed (DE) lncRNAs, 32 DE miRNAs and 981 DE mRNAs from patients with LUAD in The Cancer Genome Atlas (TCGA) database. We analysed the functions and pathways of 981 DE mRNAs using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Based on the target DE mRNAs and DE lncRNAs of DE miRNAs, we established an lncRNA-miRNA-mRNA ceRNA network, comprising 37 DE lncRNAs, 22 DE miRNAs and 212 DE mRNAs. Subsequently, we constructed a protein-protein interaction network of DE mRNAs in the ceRNA network. Among all, DE RNAs, 5 DE lncRNAs, 5 DE miRNAs and 45 DE mRNAs were confirmed found to be associated with clinical prognosis. Moreover, 3 DE lncRNAs, 4 DE miRNAs and 9 DE mRNAs in the ceRNA network were associated with clinical prognosis. We further screened 3 DE lncRNAs, 3 DE miRNAs and 3 DE mRNAs using clinical samples. These DE lncRNAs, DE miRNAs and DE mRNAs in ceRNA network may serve as independent biomarkers of LUAD metastasis.

Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective

Chinese medicine (CM) is usually prescribed as CM formula to treat disease. The lack of effective research approach makes it difficult to elucidate the molecular mechanisms of CM formula owing to its complicated chemical compounds. Network pharmacology is increasingly applied in CM formula research in recent years, which is identified suitable for the study of CM formula. In this review, we summarized the methodology of network pharmacology, including network construction, network analysis and network verification. The aim of constructing a network is to achieve the interaction between the bioactive compounds and targets and the interaction between various targets, and then find out and validate the key nodes via network analysis and network verification. Besides, we reviewed the application in CM formula research, mainly including targets discovery, bioactive compounds screening, toxicity evaluation, mechanism research and quality control research. Finally, we proposed prospective in the future and limitations of network pharmacology, expecting to provide new strategy and thinking on study for CM formula.

Network propagation: a universal amplifier of genetic associations

Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery - network propagation - suggesting that it is a powerful data transformation method of broad utility in genetic research.