It is known that long non-coding RNAs(lncRNAs)play vital roles in biological processes and contribute to the progression,development,and treatment of various diseases.Obviously,understanding associations between disea...It is known that long non-coding RNAs(lncRNAs)play vital roles in biological processes and contribute to the progression,development,and treatment of various diseases.Obviously,understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms.Nevertheless,the process of determining lncRNA-disease associations is costly,labor-intensive,and time-consuming.Hence,it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources.In this study,a collaborative filtering and graph attention network-based LncRNA-Disease Association(CFGANLDA)method was nominated to expose potential lncRNA-disease associations.First,it takes into account the advantages of using biological information from multiple sources.Next,it uses a collaborative filtering technique in order to address the sparse data problem.It also employs a graph attention network to reinforce both linear and non-linear features of the associations to advance prediction performance.The computational results indicate that CFGANLDA gains better prediction performance compared to other state-of-the-art approaches.The CFGANLDA’s area under the receiver operating characteristic curve(AUC)metric is 0.9835,whereas its area under the precision-recall curve(AUPR)metric is 0.9822.Statistical analysis using 10-fold cross-validation experiments proves that these metrics are significant.Furthermore,three case studies on prostate,liver,and stomach cancers attest to the validity of CFGANLDA performance.As a result,CFGANLDA method proves to be a valued tool for lncRNA-disease association prediction.展开更多
To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis o...To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.展开更多
Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalabi...Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalability as calculation complexity increases rapidly both in time and space when the record in the user database increases. Peer-to-peer (P2P) network has attracted much attention because of its advantage of scalability as an alternative architecture for CF systems. In this paper, authors propose a decentralized CF algorithm, called PipeCF, based on distributed hash table (DHT) method which is the most popular P2P routing algorithm because of its efficiency, scalability, and robustness. Authors also propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to improve the scalability and prediction accuracy of DHT-based CF algorithm. The experimental data show that our DHT-based CF system has better prediction accuracy, efficiency and scalability than traditional CF systems.展开更多
目的:基于网络药理学和分子对接技术,探讨经验方归元膏的活性成分防治慢性疲劳综合征(CFS)的潜在机制。方法:从TCMSP数据库、BATMAN数据库筛选归元膏的化学成分和靶点,通过GeneCards、OMIM、DisGenet数据库检索收集CFS靶点,将归元膏作...目的:基于网络药理学和分子对接技术,探讨经验方归元膏的活性成分防治慢性疲劳综合征(CFS)的潜在机制。方法:从TCMSP数据库、BATMAN数据库筛选归元膏的化学成分和靶点,通过GeneCards、OMIM、DisGenet数据库检索收集CFS靶点,将归元膏作用靶点和疾病靶点取交集,使用Cytoscape Version 3.9.1制做药物和疾病靶点韦恩图,运用STRING数据库构建蛋白质与蛋白质相互作用的PPI网络作用图,并通过Cytoscape3.8.2软件中的插件CytoNCA筛选关键靶点,并将交集靶点导入David数据库,利用微生信程序进行基因本体(GO)和京都和基因组百科全书(KEGG)富集分析,并通过Cytoscape3.8.2构建归元膏药物–有效成分–靶点和归元膏药物–成分–靶点–通路网络,最后用AutoDock vinna软件进行分子对接验证。结果:检索出归元膏活性成分有111个,其中quercetin,isoflavanone,poriferasta-7,22E-dien-3beta-ol,hancinol,orchinol等为归元膏防治CFS的关键成分,CFS相关靶点507个,其中IL-6 (白细胞介素-6),TNF (肿瘤坏死因子),STAT3 (信号转导及转录激活因子3),JUN,BCL2 (B细胞淋巴瘤/白血病-2),HIF1A (缺氧诱导因子1α亚单位),AKT1 (AKT丝氨酸/苏氨酸激酶1),CASP3 (caspase-3),MMP9 (基质金属蛋白酶9),NFKB1 (核因子kappa B亚基1)为防治CFS的关键靶点,GO富集分析示生物学过程方面可能通过对凋亡过程的负调控、细胞增殖的正调控、蛋白质磷酸化的正调控、内皮细胞增殖的正调控等,分子功能主要包括酶结合、受体结合、蛋白酶结合等;细胞功能主要包含线粒体、蛋白质复合物、细胞内膜结合的细胞器等。KEGG富集分析一共得到186条信号通路,主要与PI3K-Akt通路、卡波西肉瘤相关疱疹病毒感染、丝裂原活化蛋白激酶信号通路等有关。分子对接结果表明归元膏中的核心作用靶点和主要活性成分具备稳定性的结合活性。结论:归元膏将quercetin、poriferasta-7,22E-dien-3beta-ol、orchinol、isoflavanone等关键活性成分与IL6、TNF、STAT3、JUN、BCL2、HIF1A、AKT1和CASP3等靶点结合,通过对PI3K/Akt、MAPK、NF-κB等多条信号通路的调控,调节了CFS患者的免疫反应、细胞凋亡、代谢紊乱等多个生物学过程。通过激活抗凋亡通路、抑制促炎因子的产生和调节能量代谢,验证了该方多靶点多线路的调控,发挥了多靶点、多通路的协同作用,显著改善了CFS的症状。Objective: Based on network pharmacology and molecular docking technology, this study explores the potential mechanisms of active ingredients in the empirical formula Guiyuan Ointment for the prevention and treatment of chronic fatigue syndrome (CFS). Methods: Chemical components and targets of Guiyuan Ointment were screened from the TCMSP database and BATMAN database. CFS targets were collected through GeneCards, OMIM, and DisGenet databases. The intersection of Guiyuan Ointment’s action targets and disease targets was taken. Cytoscape Version 3.9.1 was used to create a Venn diagram of drug and disease targets. The STRING database was employed to construct a protein-protein interaction (PPI) network diagram, and key targets were screened using the CytoNCA plugin in Cytoscape 3.8.2 software. The intersection targets were imported into the David database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the microbioinformatics program. Cytoscape 3.8.2 was used to construct the Guiyuan Ointment drug-effective component-target and drug-component-target-pathway networks. Finally, molecular docking validation was conducted using AutoDock Vina software. Results: A total of 111 active components of Guiyuan Ointment were retrieved, among which quercetin, isoflavanone, poriferasta-7,22E-dien-3beta-ol, hancinol, and orchinol are key components for the prevention and treatment of CFS by Guiyuan Ointment. There are 507 CFS-related targets, with IL-6 (interleukin-6), TNF (tumor necrosis factor), STAT3 (signal transducer and activator of transcription 3), JUN, BCL2 (B-cell lymphoma/leukemia-2), HIF1A (hypoxia-inducible factor 1α subunit), AKT1 (AKT serine/threonine kinase 1), CASP3 (caspase-3), MMP9 (matrix metallopeptidase 9), and NFKB1 (nuclear factor kappa B subunit 1) being key targets for the prevention and treatment of CFS. GO enrichment analysis showed that in terms of biological processes, it may involve negative regulation of apoptosis, positive regulation of cell proliferation, positive regulation of protein phosphorylation, and positive regulation of endothelial cell proliferation, etc. The main molecular functions include enzyme binding, receptor binding, and protease binding, etc. The main cellular functions involve mitochondria, protein complexes, and cell organelles bound to the intracellular membrane, etc. KEGG enrichment analysis yielded a total of 186 signaling pathways, mainly related to the PI3K-Akt pathway, Kaposi’s sarcoma-associated herpesvirus infection, and mitogen-activated protein kinase signaling pathway, etc. The molecular docking results indicated that the core action targets and main active components in Guiyuan Ointment have stable binding activity. Conclusion: Guiyuan Ointment combines key active components such as quercetin, poriferasta-7,22E-dien-3beta-ol, orchinol, and isoflavanone with targets like IL6, TNF, STAT3, JUN, BCL2, HIF1A, AKT1, and CASP3. By regulating multiple signaling pathways such as PI3K/Akt, MAPK, and NF-κB, it modulates various biological processes in CFS patients, including immune response, cell apoptosis, and metabolic disorders. Through activating anti-apoptotic pathways, inhibiting the production of pro-inflammatory factors, and regulating energy metabolism, it verifies the multi-target and multi-pathway regulation of this formula, exerting synergistic effects on multiple targets and pathways, and significantly improving the symptoms of CFS.展开更多
基金supported by the Vietnam Ministry of Education and Training under project code B2023-SPH-14。
文摘It is known that long non-coding RNAs(lncRNAs)play vital roles in biological processes and contribute to the progression,development,and treatment of various diseases.Obviously,understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms.Nevertheless,the process of determining lncRNA-disease associations is costly,labor-intensive,and time-consuming.Hence,it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources.In this study,a collaborative filtering and graph attention network-based LncRNA-Disease Association(CFGANLDA)method was nominated to expose potential lncRNA-disease associations.First,it takes into account the advantages of using biological information from multiple sources.Next,it uses a collaborative filtering technique in order to address the sparse data problem.It also employs a graph attention network to reinforce both linear and non-linear features of the associations to advance prediction performance.The computational results indicate that CFGANLDA gains better prediction performance compared to other state-of-the-art approaches.The CFGANLDA’s area under the receiver operating characteristic curve(AUC)metric is 0.9835,whereas its area under the precision-recall curve(AUPR)metric is 0.9822.Statistical analysis using 10-fold cross-validation experiments proves that these metrics are significant.Furthermore,three case studies on prostate,liver,and stomach cancers attest to the validity of CFGANLDA performance.As a result,CFGANLDA method proves to be a valued tool for lncRNA-disease association prediction.
基金supported by the Major Science and Technology Project of Gansu Province(No.22ZD6FA021-5)the Industrial Support Project of Gansu Province(Nos.2023CYZC-19 and 2021CYZC-22)the Science and Technology Project of Gansu Province(Nos.23YFFA0074,22JR5RA137 and 22JR5RA151).
文摘To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.
文摘Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalability as calculation complexity increases rapidly both in time and space when the record in the user database increases. Peer-to-peer (P2P) network has attracted much attention because of its advantage of scalability as an alternative architecture for CF systems. In this paper, authors propose a decentralized CF algorithm, called PipeCF, based on distributed hash table (DHT) method which is the most popular P2P routing algorithm because of its efficiency, scalability, and robustness. Authors also propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to improve the scalability and prediction accuracy of DHT-based CF algorithm. The experimental data show that our DHT-based CF system has better prediction accuracy, efficiency and scalability than traditional CF systems.
文摘目的:基于网络药理学和分子对接技术,探讨经验方归元膏的活性成分防治慢性疲劳综合征(CFS)的潜在机制。方法:从TCMSP数据库、BATMAN数据库筛选归元膏的化学成分和靶点,通过GeneCards、OMIM、DisGenet数据库检索收集CFS靶点,将归元膏作用靶点和疾病靶点取交集,使用Cytoscape Version 3.9.1制做药物和疾病靶点韦恩图,运用STRING数据库构建蛋白质与蛋白质相互作用的PPI网络作用图,并通过Cytoscape3.8.2软件中的插件CytoNCA筛选关键靶点,并将交集靶点导入David数据库,利用微生信程序进行基因本体(GO)和京都和基因组百科全书(KEGG)富集分析,并通过Cytoscape3.8.2构建归元膏药物–有效成分–靶点和归元膏药物–成分–靶点–通路网络,最后用AutoDock vinna软件进行分子对接验证。结果:检索出归元膏活性成分有111个,其中quercetin,isoflavanone,poriferasta-7,22E-dien-3beta-ol,hancinol,orchinol等为归元膏防治CFS的关键成分,CFS相关靶点507个,其中IL-6 (白细胞介素-6),TNF (肿瘤坏死因子),STAT3 (信号转导及转录激活因子3),JUN,BCL2 (B细胞淋巴瘤/白血病-2),HIF1A (缺氧诱导因子1α亚单位),AKT1 (AKT丝氨酸/苏氨酸激酶1),CASP3 (caspase-3),MMP9 (基质金属蛋白酶9),NFKB1 (核因子kappa B亚基1)为防治CFS的关键靶点,GO富集分析示生物学过程方面可能通过对凋亡过程的负调控、细胞增殖的正调控、蛋白质磷酸化的正调控、内皮细胞增殖的正调控等,分子功能主要包括酶结合、受体结合、蛋白酶结合等;细胞功能主要包含线粒体、蛋白质复合物、细胞内膜结合的细胞器等。KEGG富集分析一共得到186条信号通路,主要与PI3K-Akt通路、卡波西肉瘤相关疱疹病毒感染、丝裂原活化蛋白激酶信号通路等有关。分子对接结果表明归元膏中的核心作用靶点和主要活性成分具备稳定性的结合活性。结论:归元膏将quercetin、poriferasta-7,22E-dien-3beta-ol、orchinol、isoflavanone等关键活性成分与IL6、TNF、STAT3、JUN、BCL2、HIF1A、AKT1和CASP3等靶点结合,通过对PI3K/Akt、MAPK、NF-κB等多条信号通路的调控,调节了CFS患者的免疫反应、细胞凋亡、代谢紊乱等多个生物学过程。通过激活抗凋亡通路、抑制促炎因子的产生和调节能量代谢,验证了该方多靶点多线路的调控,发挥了多靶点、多通路的协同作用,显著改善了CFS的症状。Objective: Based on network pharmacology and molecular docking technology, this study explores the potential mechanisms of active ingredients in the empirical formula Guiyuan Ointment for the prevention and treatment of chronic fatigue syndrome (CFS). Methods: Chemical components and targets of Guiyuan Ointment were screened from the TCMSP database and BATMAN database. CFS targets were collected through GeneCards, OMIM, and DisGenet databases. The intersection of Guiyuan Ointment’s action targets and disease targets was taken. Cytoscape Version 3.9.1 was used to create a Venn diagram of drug and disease targets. The STRING database was employed to construct a protein-protein interaction (PPI) network diagram, and key targets were screened using the CytoNCA plugin in Cytoscape 3.8.2 software. The intersection targets were imported into the David database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the microbioinformatics program. Cytoscape 3.8.2 was used to construct the Guiyuan Ointment drug-effective component-target and drug-component-target-pathway networks. Finally, molecular docking validation was conducted using AutoDock Vina software. Results: A total of 111 active components of Guiyuan Ointment were retrieved, among which quercetin, isoflavanone, poriferasta-7,22E-dien-3beta-ol, hancinol, and orchinol are key components for the prevention and treatment of CFS by Guiyuan Ointment. There are 507 CFS-related targets, with IL-6 (interleukin-6), TNF (tumor necrosis factor), STAT3 (signal transducer and activator of transcription 3), JUN, BCL2 (B-cell lymphoma/leukemia-2), HIF1A (hypoxia-inducible factor 1α subunit), AKT1 (AKT serine/threonine kinase 1), CASP3 (caspase-3), MMP9 (matrix metallopeptidase 9), and NFKB1 (nuclear factor kappa B subunit 1) being key targets for the prevention and treatment of CFS. GO enrichment analysis showed that in terms of biological processes, it may involve negative regulation of apoptosis, positive regulation of cell proliferation, positive regulation of protein phosphorylation, and positive regulation of endothelial cell proliferation, etc. The main molecular functions include enzyme binding, receptor binding, and protease binding, etc. The main cellular functions involve mitochondria, protein complexes, and cell organelles bound to the intracellular membrane, etc. KEGG enrichment analysis yielded a total of 186 signaling pathways, mainly related to the PI3K-Akt pathway, Kaposi’s sarcoma-associated herpesvirus infection, and mitogen-activated protein kinase signaling pathway, etc. The molecular docking results indicated that the core action targets and main active components in Guiyuan Ointment have stable binding activity. Conclusion: Guiyuan Ointment combines key active components such as quercetin, poriferasta-7,22E-dien-3beta-ol, orchinol, and isoflavanone with targets like IL6, TNF, STAT3, JUN, BCL2, HIF1A, AKT1, and CASP3. By regulating multiple signaling pathways such as PI3K/Akt, MAPK, and NF-κB, it modulates various biological processes in CFS patients, including immune response, cell apoptosis, and metabolic disorders. Through activating anti-apoptotic pathways, inhibiting the production of pro-inflammatory factors, and regulating energy metabolism, it verifies the multi-target and multi-pathway regulation of this formula, exerting synergistic effects on multiple targets and pathways, and significantly improving the symptoms of CFS.
