Study on prediction of compound-target-disease network of Chuanxiong Rhizoma based on random forest algorithm

2014 ◽  
Keyword(s):  
Random Forest ◽  
Random Forest Algorithm ◽  
Disease Network ◽  
Target Disease ◽  
Compound Target

scholarly journals Study on the Mechanism of Lianpu Drink for the Treatment of Chronic Gastritis Based on Network Pharmacology

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shuhan Zhou ◽  
Yanjun Duan ◽  
Yu Deng ◽  
Miao Wang ◽  
Chaoqun Huang ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Signaling Pathway ◽  
Chronic Gastritis ◽  
Mechanisms Of Action ◽  
Network Pharmacology ◽  
Biological Pathway ◽  
Disease Network ◽  
Target Disease ◽  
Compound Target

Chronic gastritis (CG) places a considerable burden on the healthcare system worldwide. Traditional Chinese Medicine (TCM) formulas characterized by multicompounds and multitargets have been acknowledged with striking effects in the treatment of CG in China’s history. Nevertheless, their accurate mechanisms of action are still ambiguous. In this study, we analyzed the effective compounds, potential targets, and related biological pathway of Lianpu Drink (LPD), a TCM formula which has been reported to have a therapeutic effect on CG, by contrasting a “compound-target-disease” network. According to the results, 92 compounds and 5762 putative targets of LPD were screened; among them, 8 compounds derived from different herbs in LPD and 30 common targets related to LPD and CG were selected as candidate compounds and precision targets, respectively. Meanwhile, the predicted common targets were verified by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis and pharmacological experiments. The results demonstrated that quercetin, ephedrine, trigonelline, crocetin, and β-sitosterol were major effective compounds of LPD responsible for the CG treatment by inhibiting the activation of the JAK 2-STAT 3 signaling pathway to reduce the expressions of cyclin D1 and Bcl-2 proteins. The study provides evidence for the mechanism of understanding of LPD for the treatment of CG.

scholarly journals Network Pharmacology-Based Investigation into the Mechanisms of Quyushengxin Formula for the Treatment of Ulcerative Colitis

2019 ◽  
Vol 2019 ◽  
pp. 1-22
Author(s):  
Haojie Yang ◽  
Ying Li ◽  
Sichen Shen ◽  
Dan Gan ◽  
Changpeng Han ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Bioactive Compounds ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Disease Network ◽  
Pathway Network ◽  
Similarity Algorithm ◽  
Target Network ◽  
Target Disease ◽  
Compound Target

Objective. Ulcerative colitis (UC) is a chronic idiopathic inflammatory bowel disease whose treatment strategies remain unsatisfactory. This study aims to investigate the mechanisms of Quyushengxin formula acting on UC based on network pharmacology. Methods. Ingredients of the main herbs in Quyushengxin formula were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Absorption, distribution, metabolism, and excretion properties of all ingredients were evaluated for screening out candidate bioactive compounds in Quyushengxin formula. Weighted ensemble similarity algorithm was applied for predicting direct targets of bioactive ingredients. Functional enrichment analyses were performed for the targets. In addition, compound-target network, target-disease network, and target-pathway network were established via Cytoscape 3.6.0 software. Results. A total of 41 bioactive compounds in Quyushengxin formula were selected out from the TCMSP database. These bioactive compounds were predicted to target 94 potential proteins by weighted ensemble similarity algorithm. Functional analysis suggested these targets were closely related with inflammatory- and immune-related biological progresses. Furthermore, the results of compound-target network, target-disease network, and target-pathway network indicated that the therapeutic effects of Quyushengxin on UC may be achieved through the synergistic and additive effects. Conclusion. Quyushengxin may act on immune and inflammation-related targets to suppress UC progression in a synergistic and additive manner.

scholarly journals Identification of Key Pathways and Targets of Kai Xin San in The Treatment of Alzheimer's Disease Based On a Network Pharmacology Approach and Experimental Validation

2021 ◽  
Author(s):  
Rong Yang ◽  
Kan Wang ◽  
Tuo Li ◽  
Mianmian Liao ◽  
Mingwang Kong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Binding Affinity ◽  
Venn Diagram ◽  
Disease Network ◽  
Target Proteins ◽  
Target Disease ◽  
Compound Target

Abstract Background: Alzheimer's disease (AD) is the commonest neurodegenerative disease characterized with a progressive loss of cognitive functions and memory decline. Kai Xin San (KXS), a traditional Chinese herbal classic prescription, has been used to ameliorate cognitive dysfunction for thousands of years. However, its specific pharmacological molecular mechanisms have not been fully clarified.Methods: The ingredients of KXS and their corresponding targets were firstly screened from ETCM database. AD-related target proteins were obtained from Malacards database and DisGeNet database. Venn diagram was used to intersect the common targets between KXS and AD. Then, key ingredients and key targets were identified from compound-target-disease network and protein-protein interaction (PPI) network analysis respectively. Moreover, the binding affinity between the key ingredients and targets were verified by molecular docking. KEGG enrichment analysis further predicted the potential key signaling pathway involved in the treatment of KXS on AD, and the predicted signaling pathway was validated via experimental approach.Results: A total of 38 ingredients and 469 corresponding targets were screened, and 264 target proteins associated with AD were obtained. Compound-target-disease network and PPI identified the key active ingredients and targets, which correlate with the treatment of KXS on AD. Molecular docking revealed a good binding affinity between key ingredients and targets. KEGG pathway analysis suggested the potential effect of KXS in treatment of AD via Aβ-GSK3β-Tau pathway. Aβ1-42-injected induced a decline in spatial learning and memory and upregulated the expression of GSK3β and CDK5 along with the downregulated PP1 and PP2 expression. However, KXS significantly improve the cognitive deficits induced by Aβ1-42, decrease the GSK3β and CDK5 levels and increase the expression of PP1 and PP2.Conclusions: Our research elucidated that KXS exerted neuroprotective effects through regulating the Aβ-GSK3β-Tau signaling pathway, which provided a novel insight into the therapeutic mechanism of KXS in treatment of AD.

scholarly journals Systems-Pharmacology-Based Identification of Antitumor Necrosis Factor Effect in Mimeng Flower Decoction for the Treatment of Diabetic Retinopathy

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yingzi Li ◽  
Ying Huang ◽  
Changsen Tu
Keyword(s):  
Diabetic Retinopathy ◽  
Binding Affinity ◽  
Diabetic Rat ◽  
Systems Pharmacology ◽  
Disease Network ◽  
Active Compounds ◽  
Hub Gene ◽  
Target Disease ◽  
Necrosis Factor ◽  
Compound Target

The traditional Chinese medicine of Mimeng flower decoction (MFD) is effective in treating diabetic retinopathy (DR), but the mechanism is still unclear. This study aims at investigating the mechanism of MFD in treating DR. First, active compounds in MFD were filtered out by the systems pharmacology method and used as bait to fish potential targets. The common genes between the targets and DR-related genes were selected to construct the compound-target-disease network and identify the network hub gene as a key gene. Molecular docking was simulated to assess the binding affinity of active compounds towards the gene protein. Streptozotocin- (STZ-) induced diabetic rat model was administered to evaluate the efficacy of MFD in treating DR and its effects on retinal gene expression. Finally, 53 active compounds were screened out from the seven herbs in MFD, with a total of 136 targets. After intersecting with 210 DR-related genes, 21 common genes were applied to construct the network, and tumor necrosis factor (TNF) was identified as the hub gene. The active compounds of acacetin, kaempferol, luteolin, and quercetin showed a good binding affinity towards TNF (C-score ≥ 4). In diabetic rats, MFD treatment reversed the retinal impairment and decreased retinal TNF expression significantly. In conclusion, this study adopted the method of systems pharmacology to screen out active compounds and construct the compound-target-disease network and found that MFD could ameliorate DR by downregulating the network hub gene of TNF.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

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