Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the di...Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.展开更多
Objective: To explore the anti-cancer mechanism of active ingredients of Astragalus membranaceus (AM) through network pharmacology. Methods: TCMSP, PubChem, STICTH and GeneCards databases were used to predict and scre...Objective: To explore the anti-cancer mechanism of active ingredients of Astragalus membranaceus (AM) through network pharmacology. Methods: TCMSP, PubChem, STICTH and GeneCards databases were used to predict and screen the main active ingredients and anti-cancer targets of AM. Active ingredient-target-disease network was constructed by Cytoscape 3.7.0 software, and protein interaction network was constructed by STRING platform. KEGG signaling pathway and GO biological process of targets were analyzed by Bioconductor database. Results: Twenty-four active ingredients were screened from AM, which acted on 106 cancer targets such as PTGS, NCOA2, ADRB2, PRSS1, NOS2, NOS3, GABRA1. Through these targets, the anti-cancer effect of AM mainly acts on small cell lung cancer, colorectal cancer, thyroid cancer, breast cancer, non-small cell lung cancer, hepatocellular carcinoma, pancreatic cancer, gastric cancer, endometrial cancer, enriched in chemical carcinogenesis, Platinum drug resistance, Epstein-Barr virus infection, TNF signaling pathway, Toll-like receptor signaling pathway, p53 signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, and PI3K - Akt signaling pathway. Conclusion: This study found that the main anti-cancer compounds of AM are kaempferol, quercetin, 7-O-methylisomucronulatol, formononetin, isorhamnetin, Calycosin, 3,9-di-O-methylnissolin. The main targets include PTGS, PTGS1, NCOA2, ADRB2, PRSS1, NOS2, NOS3, GABRA1, F2. The mechanisms involved in anticancer could be summarized as following: blocking the chemical carcinogenesis, reversing the platinum drug resistance, anti - Epstein - Barrvirus infection, and inhibiting cell proliferation related signaling pathways, such as TNF signaling pathway, Tolllike receptor signaling pathway, p53 signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, PI3K - AKT signaling pathway.展开更多
基金supported by the National Natural Science Foundation of China(No.51877013),(ZJ),(http://www.nsfc.gov.cn/)the Natural Science Foundation of Jiangsu Province(No.BK20181463),(ZJ),(http://kxjst.jiangsu.gov.cn/)sponsored by Qing Lan Project of Jiangsu Province(no specific grant number),(ZJ),(http://jyt.jiangsu.gov.cn/).
文摘Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.
基金National Natural Science Foundation of China Regional Science Fund Project (81673862, 81660833, 81760814)Guizhou Provincial Department of Education Project (Qian jiao yan he GZS ZI[2016]08)+2 种基金Guizhou Postgraduate Workstation Program (Educational Hall) Project (Qian jiao yan he JYSZ ZI[2014]018)Guizhou Science and Technology Department Project [Qian ke he ren cai(2016)4032]Guizhou Provincial Organization Department Project (Qian ren ban fa[2018] No. 3).
文摘Objective: To explore the anti-cancer mechanism of active ingredients of Astragalus membranaceus (AM) through network pharmacology. Methods: TCMSP, PubChem, STICTH and GeneCards databases were used to predict and screen the main active ingredients and anti-cancer targets of AM. Active ingredient-target-disease network was constructed by Cytoscape 3.7.0 software, and protein interaction network was constructed by STRING platform. KEGG signaling pathway and GO biological process of targets were analyzed by Bioconductor database. Results: Twenty-four active ingredients were screened from AM, which acted on 106 cancer targets such as PTGS, NCOA2, ADRB2, PRSS1, NOS2, NOS3, GABRA1. Through these targets, the anti-cancer effect of AM mainly acts on small cell lung cancer, colorectal cancer, thyroid cancer, breast cancer, non-small cell lung cancer, hepatocellular carcinoma, pancreatic cancer, gastric cancer, endometrial cancer, enriched in chemical carcinogenesis, Platinum drug resistance, Epstein-Barr virus infection, TNF signaling pathway, Toll-like receptor signaling pathway, p53 signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, and PI3K - Akt signaling pathway. Conclusion: This study found that the main anti-cancer compounds of AM are kaempferol, quercetin, 7-O-methylisomucronulatol, formononetin, isorhamnetin, Calycosin, 3,9-di-O-methylnissolin. The main targets include PTGS, PTGS1, NCOA2, ADRB2, PRSS1, NOS2, NOS3, GABRA1, F2. The mechanisms involved in anticancer could be summarized as following: blocking the chemical carcinogenesis, reversing the platinum drug resistance, anti - Epstein - Barrvirus infection, and inhibiting cell proliferation related signaling pathways, such as TNF signaling pathway, Tolllike receptor signaling pathway, p53 signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, PI3K - AKT signaling pathway.
