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Zhao BW, He YZ, Su XR, Yang Y, Li GD, Huang YA, Hu PW, You ZH, Hu L. Motif-Aware miRNA-Disease Association Prediction Via Hierarchical Attention Network. IEEE J Biomed Health Inform 2024; PP:1-14. [PMID: 38557614 DOI: 10.1109/jbhi.2024.3383591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives. The data and codes are available at https://github.com/stevejobws/MotifMDA.
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Yang GY, He ZW, Tang YC, Yuan F, Cao MB, Ren YP, Li YX, Su XR, Yao ZC, Deng MH. Unraveling the efficacy network: A network meta-analysis of adjuvant external beam radiation therapy methods after hepatectomy. World J Gastrointest Surg 2024; 16:205-214. [PMID: 38328333 PMCID: PMC10845281 DOI: 10.4240/wjgs.v16.i1.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/09/2023] [Accepted: 01/09/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Primary liver cancer is a malignant tumor with a high recurrence rate that significantly affects patient prognosis. Postoperative adjuvant external radiation therapy (RT) has been shown to effectively prevent recurrence after liver cancer resection. However, there are multiple RT techniques available, and the differential effects of these techniques in preventing postoperative liver cancer recurrence require further investigation. AIM To assess the advantages and disadvantages of various adjuvant external RT methods after liver resection based on overall survival (OS) and disease-free survival (DFS) and to determine the optimal strategy. METHODS This study involved network meta-analyses and followed the PRISMA guidelines. The data of qualified studies published before July 10, 2023, were collected from PubMed, Embase, the Web of Science, and the Cochrane Library. We included relevant studies on postoperative external beam RT after liver resection that had OS and DFS as the primary endpoints. The magnitudes of the effects were determined using risk ratios with 95% confidential intervals. The results were analyzed using R software and STATA software. RESULTS A total of 12 studies, including 1265 patients with hepatocellular carcinoma (HCC) after liver resection, were included in this study. There was no significant heterogeneity in the direct paired comparisons, and there were no significant differences in the inclusion or exclusion criteria, intervention measures, or outcome indicators, meeting the assumptions of heterogeneity and transitivity. OS analysis revealed that patients who underwent stereotactic body radiotherapy (SBRT) after resection had longer OS than those who underwent intensity modulated radiotherapy (IMRT) or 3-dimensional conformal RT (3D-CRT). DFS analysis revealed that patients who underwent 3D-CRT after resection had the longest DFS. Patients who underwent IMRT after resection had longer OS than those who underwent 3D-CRT and longer DFS than those who underwent SBRT. CONCLUSION HCC patients who undergo liver cancer resection must consider distinct advantages and disadvantages when choosing between SBRT and 3D-CRT. IMRT, a RT technique that is associated with longer OS than 3D-CRT and longer DFS than SBRT, may be a preferred option.
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Affiliation(s)
- Gao-Yuan Yang
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Zhi-Wei He
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Yong-Chang Tang
- Department of General Surgery, Qilu Hospital, Shandong University, Jinan 250012, Shandong Province, China
| | - Feng Yuan
- Department of General Surgery, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, Guangdong Province, China
| | - Ming-Bo Cao
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Yu-Peng Ren
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Yu-Xuan Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Xiao-Rui Su
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Zhi-Cheng Yao
- Department of Hepatobiliary and Pancreatic Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Mei-Hai Deng
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
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Zhao BW, Su XR, Yang Y, Li DX, Li GD, Hu PW, Zhao YG, Hu L. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks. Methods 2023; 220:106-114. [PMID: 37972913 DOI: 10.1016/j.ymeth.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/13/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yue Yang
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Dong-Xu Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Guo-Dong Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yong-Gang Zhao
- Department of Orthopaedic Surgery (hand and foot trauma), People's Hospital of Dongxihu, Wuhan 420100, China.
| | - Lun Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
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Li DX, Zhou P, Zhao BW, Su XR, Li GD, Zhang J, Hu PW, Hu L. Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks. BMC Bioinformatics 2023; 24:451. [PMID: 38030973 PMCID: PMC10685597 DOI: 10.1186/s12859-023-05574-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .
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Affiliation(s)
- Dong-Xu Li
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Peng Zhou
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Guo-Dong Li
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Jun Zhang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China.
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Zhang ZH, Yang CT, Su XR, Li YP, Zhang XJ, Wang SJ, Cong B. CCK1R2R -/- ameliorates myocardial damage caused by unpredictable stress via altering fatty acid metabolism. Stress 2023; 26:2254566. [PMID: 37665601 DOI: 10.1080/10253890.2023.2254566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/27/2023] [Indexed: 09/05/2023] Open
Abstract
The heart is the main organ of the circulatory system and requires fatty acids to maintain its activity. Stress is a contributor to aggravating cardiovascular diseases and even death, and exacerbates the abnormal lipid metabolism. The cardiac metabolism may be disturbed by stress. Cholecystokinin (CCK), which is a classical peptide hormone, and its receptor (CCKR) are expressed in myocardial cells and affect cardiovascular function. Nevertheless, under stress, the exact role of CCKR on cardiac function and cardiac metabolism is unknown and the mechanism is worth exploring. After unpredictable stress, a common stress-inducing model that induces the development of mood disorders such as anxiety and reduces motivated behavior, we found that the abnormal contraction and diastole of the heart, myocardial injury, oxidative stress and inflammation of mice were aggravated. Cholecystokinin A receptor and cholecystokinin B receptor knockout (CCK1R2R-/-) significantly reversed these changes. Mechanistically, fatty acid metabolism was found to be altered in CCK1R2R-/- mice. Differential metabolites, especially L-tryptophan, L-aspartic acid, cholesterol, taurocholic acid, ADP, oxoglutaric acid, arachidonic acid and 17-Hydroxyprogesterone, influenced cardiac function after CCK1R2R knockout and unpredictable stress. We conclude that CCK1R2R-/- ameliorated myocardial damage caused by unpredictable stress via altering fatty acid metabolism.
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Affiliation(s)
- Zhi-Hua Zhang
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
- Hebei Chest Hospital, Hebei Provincial Key Laboratory of Lung Disease, Hebei, P.R. China
| | - Chen-Teng Yang
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
| | - Xiao-Rui Su
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
| | - Ya-Ping Li
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
| | - Xiao-Jing Zhang
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
| | - Song-Jun Wang
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
| | - Bin Cong
- Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, College of Forensic Medicine, Hebei Medical University, Hebei, P.R. China
- Hainan Tropical Forensic Medicine Academician Workstation, Haikou, Hainan, P.R. China
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Wang SJ, Liu BR, Zhang F, Su XR, Li YP, Yang CT, Zhang ZH, Cong B. The amino acid metabolomics signature of differentiating myocardial infarction from strangulation death in mice models. Sci Rep 2023; 13:14999. [PMID: 37696922 PMCID: PMC10495377 DOI: 10.1038/s41598-023-41819-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
This study differentiates myocardial infarction (MI) and strangulation death (STR) from the perspective of amino acid metabolism. In this study, MI mice model via subcutaneous injection of isoproterenol and STR mice model by neck strangulation were constructed, and were randomly divided into control (CON), STR, mild MI (MMI), and severe MI (SMI) groups. The metabolomics profiles were obtained by liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics. Principal component analysis, partial least squares-discriminant analysis, volcano plots, and heatmap were used for discrepancy metabolomics analysis. Pathway enrichment analysis was performed and the expression of proteins related to metabolomics was detected using immunohistochemical and western blot methods. Differential metabolites and metabolite pathways were screened. In addition, we found the expression of PPM1K was significantly reduced in the MI group, but the expression of p-mTOR and p-S6K1 were significantly increased (all P < 0.05), especially in the SMI group (P < 0.01). The expression of Cyt-C was significantly increased in each group compared with the CON group, especially in the STR group (all P < 0.01), and the expression of AMPKα1 was significantly increased in the STR group (all P < 0.01). Our study for the first time revealed significant differences in amino acid metabolism between STR and MI.
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Affiliation(s)
- Song-Jun Wang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, No. 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050017, Hebei, China
| | - Bing-Rui Liu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, No. 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050017, Hebei, China
| | - Fu Zhang
- Forensic Pathology Lab, Guangdong Public Security Department, Guangzhou, China
| | - Xiao-Rui Su
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, No. 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050017, Hebei, China
| | - Ya-Ping Li
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, No. 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050017, Hebei, China
| | - Chen-Teng Yang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, No. 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050017, Hebei, China
| | - Zhi-Hua Zhang
- Department of Science and Education, Hebei Chest Hospital, No. 372 Shengli North Street, Chang'an District, Shijiazhuang, 050041, Hebei, China.
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, No. 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050017, Hebei, China.
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Zhao BW, Su XR, Hu PW, Huang YA, You ZH, Hu L. iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network. Bioinformatics 2023; 39:btad451. [PMID: 37505483 PMCID: PMC10397422 DOI: 10.1093/bioinformatics/btad451] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 06/12/2023] [Accepted: 07/27/2023] [Indexed: 07/29/2023] Open
Abstract
MOTIVATION The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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Wang SJ, Liu BR, Zhang F, Li YP, Su XR, Yang CT, Cong B, Zhang ZH. Abnormal fatty acid metabolism and ceramide expression may discriminate myocardial infarction from strangulation death: A pilot study. Tissue Cell 2023; 80:101984. [PMID: 36434828 DOI: 10.1016/j.tice.2022.101984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/16/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022]
Abstract
Determining myocardial infarction (MI) and mechanical asphyxia (MA) was one of the most challenging tasks in forensic practice. The present study aimed to investigate the potential of fatty acid (FAs) metabolism, and lipid alterations in determining MI and MA. MA and MI mouse models were constructed, and metabolic profiles were obtained by LC-MS-based untargeted metabolomics. The metabolic alterations were explored using the PCA, OPLS-DA, the Wilcoxon test, and fold change analysis. The contents of lipid droplets (LDs) were detected by the transmission scanning electron microscope and Oil red O staining. The immunohistochemical assay was performed to detect CD36 and dysferlin. The ceramide was assessed by LC-MS. PCA showed considerable differences in the metabolite profiles, and the well-fitting OPLS-DA model was developed to screen differential metabolites. Thereinto, 9 metabolites in the MA were reduced, while metabolites were up- and down-regulated in MI. The increased CD36 suggested that MI and MA could enhance the intake of FAs and disturb energy metabolism. The increased LDs, decreased dysferlin, and increased ceramide (C18:0, C22:0, and C24:0) were observed in MI groups, confirming the lipid deposition. The present study indicated significant differences in myocardial FAs metabolism and lipid alterations between MI and MA, suggesting that FAs metabolism and related proteins, certain ceramide may harbor the potential as biomarkers for discrimination of MI and MA.
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Affiliation(s)
- Song-Jun Wang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China.
| | - Bing-Rui Liu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China.
| | - Fu Zhang
- Forensic Pathology Lab, Guangdong Public Security Department, China.
| | - Ya-Ping Li
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China.
| | - Xiao-Rui Su
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China.
| | - Chen-Teng Yang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China.
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China.
| | - Zhi-Hua Zhang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, China; HeBei Chest Hospital, China.
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Zhang ML, Zhao BW, Su XR, He YZ, Yang Y, Hu L. RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction. BMC Bioinformatics 2022; 23:516. [PMID: 36456957 PMCID: PMC9713188 DOI: 10.1186/s12859-022-05069-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. METHODS In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. RESULTS To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.
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Affiliation(s)
- Meng-Long Zhang
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Bo-Wei Zhao
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Xiao-Rui Su
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Yi-Zhou He
- grid.162110.50000 0000 9291 3229School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Yue Yang
- grid.162110.50000 0000 9291 3229School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lun Hu
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
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10
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Zhao BW, Su XR, Hu PW, Ma YP, Zhou X, Hu L. A geometric deep learning framework for drug repositioning over heterogeneous information networks. Brief Bioinform 2022; 23:6692552. [PMID: 36125202 DOI: 10.1093/bib/bbac384] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 12/14/2022] Open
Abstract
Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Peng-Wei Hu
- Merck China Innovation Hub, Shanghai 200000, China
| | - Yu-Peng Ma
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Xi Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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11
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Wang L, Wong L, You ZH, Huang DS, Su XR, Zhao BW. NSECDA: Natural Semantic Enhancement for CircRNA-Disease Association Prediction. IEEE J Biomed Health Inform 2022; 26:5075-5084. [PMID: 35976848 DOI: 10.1109/jbhi.2022.3199462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Increasing evidence suggest that circRNA, as one of the most promising emerging biomarkers, has a very close relationship with diseases. Exploring the relationship between circRNA and diseases can provide novel perspective for diseases diagnosis and pathogenesis. The existing circRNA-disease association (CDA) prediction models, however, generally treat the data attributes equally, do not pay special attention to the attributes with more significant influence, and do not make full use of the correlation and symbiosis between attributes to dig into the latent semantic information of the data. Therefore, in response to the above problems, this paper proposes a natural semantic enhancement method NSECDA to predict CDA. In practical terms, we first recognize the circRNA sequence as a biological language, and analyze its natural semantic properties through the natural language understanding theory; then integrate it with disease attributes, circRNA and disease Gaussian Interaction Profile (GIP) kernel attributes, and use Graph Attention Network (GAT) to focus on the influential attributes, so as to mine the deeply hidden features; finally, the Rotation Forest (RoF) classifier was used to accurately determine CDA. In the gold standard data set CircR2Disease, NSECDA achieved 92.49% accuracy with 0.9225 AUC score. In comparison with the non-natural semantic enhancement model and other classifier models, NSECDA also shows competitive performance. Additionally, 25 of the CDA pairs with unknown associations in the top 30 prediction scores of NSECDA have been proven by newly reported studies. These achievements suggest that NSECDA is an effective model to predict CDA, which can provide credible candidate for subsequent wet experiments, thus significantly reducing the scope of investigations.
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12
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Su XR, Hu L, You ZH, Hu PW, Zhao BW. Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction. BMC Bioinformatics 2022; 23:234. [PMID: 35710342 PMCID: PMC9205098 DOI: 10.1186/s12859-022-04766-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/27/2022] [Indexed: 01/02/2023] Open
Abstract
Background Protein–protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system. Methods In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs. Results To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network. Conclusion The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.
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Affiliation(s)
- Xiao-Rui Su
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
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13
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Zhao BW, Hu L, You ZH, Wang L, Su XR. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform 2021; 23:6456295. [PMID: 34891172 DOI: 10.1093/bib/bbab515] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 12/20/2022] Open
Abstract
Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 530007, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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14
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Yi HC, You ZH, Wang L, Su XR, Zhou X, Jiang TH. In silico drug repositioning using deep learning and comprehensive similarity measures. BMC Bioinformatics 2021; 22:293. [PMID: 34074242 PMCID: PMC8170943 DOI: 10.1186/s12859-020-03882-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. METHODS In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions. RESULTS The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. CONCLUSION The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.
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Affiliation(s)
- Hai-Cheng Yi
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.
| | - Lei Wang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xi Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Tong-Hai Jiang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
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15
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Su XR, You ZH, Hu L, Huang YA, Wang Y, Yi HC. An Efficient Computational Model for Large-Scale Prediction of Protein-Protein Interactions Based on Accurate and Scalable Graph Embedding. Front Genet 2021; 12:635451. [PMID: 33719344 PMCID: PMC7953052 DOI: 10.3389/fgene.2021.635451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/25/2021] [Indexed: 11/23/2022] Open
Abstract
Protein–protein interaction (PPI) is the basis of the whole molecular mechanisms of living cells. Although traditional experiments are able to detect PPIs accurately, they often encounter high cost and require more time. As a result, computational methods have been used to predict PPIs to avoid these problems. Graph structure, as the important and pervasive data carriers, is considered as the most suitable structure to present biomedical entities and relationships. Although graph embedding is the most popular approach for graph representation learning, it usually suffers from high computational and space cost, especially in large-scale graphs. Therefore, developing a framework, which can accelerate graph embedding and improve the accuracy of embedding results, is important to large-scale PPIs prediction. In this paper, we propose a multi-level model LPPI to improve both the quality and speed of large-scale PPIs prediction. Firstly, protein basic information is collected as its attribute, including positional gene sets, motif gene sets, and immunological signatures. Secondly, we construct a weighted graph by using protein attributes to calculate node similarity. Then GraphZoom is used to accelerate the embedding process by reducing the size of the weighted graph. Next, graph embedding methods are used to learn graph topology features from the reconstructed graph. Finally, the linear Logistic Regression (LR) model is used to predict the probability of interactions of two proteins. LPPI achieved a high accuracy of 0.99997 and 0.9979 on the PPI network dataset and GraphSAGE-PPI dataset, respectively. Our further results show that the LPPI is promising for large-scale PPI prediction in both accuracy and efficiency, which is beneficial to other large-scale biomedical molecules interactions detection.
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Affiliation(s)
- Xiao-Rui Su
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Yu-An Huang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Yi Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
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16
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Xie TA, Han MY, Su XR, Li HH, Chen JC, Guo XG. Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis. J Cell Mol Med 2020; 24:12225-12230. [PMID: 32924263 PMCID: PMC7579704 DOI: 10.1111/jcmm.15862] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 08/04/2020] [Accepted: 08/10/2020] [Indexed: 02/06/2023] Open
Affiliation(s)
- Tian-Ao Xie
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Meng-Yi Han
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Xiao-Rui Su
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Hou-He Li
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Ji-Chun Chen
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Xu-Guang Guo
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China.,Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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17
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Su XR, Xue YY, Li Q. [Advances in the research of antibacterial properties of silver ion and its application in wound treatment]. Zhonghua Shao Shang Za Zhi 2018; 34:183-186. [PMID: 29609281 DOI: 10.3760/cma.j.issn.1009-2587.2018.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Nowadays, antibacterial products containing silver ion are widely used in clinical wound treatment. The concentration of silver ion in products, pH value, and other factors may affect the release of silver ion and its antibacterial effects. In the treatment of clinical wound, silver ion product plays a good role in anti-infection, promoting healing and reducing medical expenses. In this paper, the related applications of silver ion products in wound surface are analyzed, and the antibacterial properties of silver ion and its therapeutic effects in wound treatment are summarized.
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Affiliation(s)
- X R Su
- Key Laboratory of Environmental Medicine and Engineering, Ministry of Education, School of Public Health & Collaborative Innovation Center of Suzhou Nano Science and Technology, Southeast University, Jiangsu Key Laboratory for Biomaterials and Devices, Nanjing 210009, China
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18
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Kuang XY, Jiang XF, Chen C, Su XR, Shi Y, Wu JR, Zhang P, Zhang XL, Cui YH, Ping YF, Bian XW. Expressions of glia maturation factor-β by tumor cells and endothelia correlate with neovascularization and poor prognosis in human glioma. Oncotarget 2018; 7:85750-85763. [PMID: 26515590 PMCID: PMC5349871 DOI: 10.18632/oncotarget.5509] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 10/13/2015] [Indexed: 11/30/2022] Open
Abstract
Glia maturation factor-β (GMF-β) has been reported to promote glial differentiation, and act as a negative prognostic indicator in certain cancers. However, its roles in glioma progression remain unclear. Since neurogenesis and vasculogenesis were proved to share some common regulators during gliomagenesis, we aim to explore the potential impact of GMF-β on tumor neovascularization and patient survival in glioma. In this study, we first detected GMF-β expression not only in tumor cells but also in microvascular endothelia by double immunohistochemical staining. Both tumoral and endothelial GMF-β expression levels were positively correlated with tumor grade and microvessel density (MVD), while negatively associated with poor prognoses of the patients. Interestingly, multivariate analysis demonstrated that endothelial GMF-β expression level was the only independent predictor of progression-free and overall survival of glioma patients. The results of in vitro angiogenesis assay showed that GMF-β knockdown significantly inhibited tubulogenesis of human U87 glioblastoma cells. Furthermore, GMF-β knockdown suppressed tumor growth and the formation of human-CD31 positive (glioma cell-derived) microvessels in a mouse orthotopic U87 glioma model. Our results demonstrated that GMF-β is an important player in glioma progression via promoting neovascularization. GMF-β may therefore be a novel prognostic marker as well as a potential therapeutic target for glioma.
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Affiliation(s)
- Xiao-Yan Kuang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xue-Feng Jiang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Cong Chen
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xiao-Rui Su
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Yu Shi
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jin-Rong Wu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Peng Zhang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xin-Li Zhang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China.,Division of Growth and Development and Section of Orthodontics, School of Dentistry, University of California, Los Angeles, CA, USA
| | - You-Hong Cui
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Yi-Fang Ping
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xiu-Wu Bian
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.,Key Laboratory of Tumor Immunology and Pathology of Ministry of Education, Southwest Hospital, Third Military Medical University, Chongqing, China.,Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
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19
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Sun YN, Su XR, Xu TJ, Li TW. Genomic characterization and sequence diversity of the β2-microglobulin gene in the miiuy croaker, Miichthys miiuy. Genet Mol Res 2015; 14:10249-57. [PMID: 26345962 DOI: 10.4238/2015.august.28.9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
β2-Microglobulin (β2m) is related to major histocompatibility complex class I alpha chains, and forms cell-surface glycoproteins that mediate a variety of functions in immune defense. In general, β2m has no isoforms and is not polymorphic in higher vertebrates, but polymorphisms between different alleles have been found in some fish species. In this study, full-length β2m cDNA and genomic sequences were cloned from the miiuy croaker (Miichthys miiuy). The miiuy croaker β2m gene shares many of the same characteristics as other fish species. Three exons and two introns were identified in the miiuy croaker β2m gene; these genomic structural features are similar to those present in other fish. The deduced β2m amino acid sequence exhibited 34.7-90.1% identity with mammal and teleost β2m amino acid sequences. Sequence polymorphism analysis in six individuals identified three alleles that encoded two proteins, confirming that β2m polymorphisms exist in this species. Phylogenetic analysis elucidated the evolutionary history of the β2m protein among warm-blooded vertebrates and bony fish.
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Affiliation(s)
- Y N Sun
- School of Marine Sciences, Ningbo University, Ningbo, Zhejiang Province, China
| | - X R Su
- School of Marine Sciences, Ningbo University, Ningbo, Zhejiang Province, China
| | - T J Xu
- College of Marine Science, Zhejiang Ocean University, Zhoushan, Zhejiang Province, China
| | - T W Li
- School of Marine Sciences, Ningbo University, Ningbo, Zhejiang Province, China
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20
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Wang ZH, Yang JQ, Zhang DJ, Zhou J, Zhang CD, Su XR, Li TW. Composition and structure of microbial communities associated with different domestic sewage outfalls. Genet Mol Res 2014; 13:7542-52. [PMID: 25222254 DOI: 10.4238/2014.september.12.21] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The diversity of microbiota in waste waters has not been thoroughly examined, despite the potential impact of microbes on effluent quality. Wastewater microbial communities harbor pathogenic bacteria, viruses, and parasites. To study microbial communities in domestic sewage outfalls, 454 pyrosequencing technology was used to investigate the composition of microbial communities associated with municipal wastewater during different seasons sampled over the course of one year. A total of 195,103 16S rRNA gene sequences were obtained from 20 samples. The R software was used to calculate the number of indices describing the alpha diversity associated with each bacterial assemblage. In this study, the a-diversity index (H', D, J), in which higher numbers represent more diversity, was found to change with seasonal cycle. The diversity of bacterial assemblages was high in all samples, indicating that species diversity was also very high. The taxonomic composition of the assemblages varied considerably among samples, with some dominated by Proteobacteria, while others were dominated by Bacteroidetes or Firmicutes. In 2 samples, the relative prevalence of Proteobacteria exceeded 90%. α-Proteobacteria, b-proteobacteria, and g-proteobacteria represented 90% or more of all Proteobacteria. The present characterization of wastewater from five sewage outfalls indicated the presence of some pathogenic bacteria. The g-Proteobacteria in sewage wastefalls identified in this study included Enterobacteriaceae, Vibrionaceae, Pseudomonadaceae, Salmonella, Yersinia, Vibrio, and Pseudomonas aeruginosa.
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Affiliation(s)
- Z H Wang
- School of Marine Science, Ningbo University, Ningbo, Zhejiang Province, China
| | - J Q Yang
- North China Sea Branch of The State Oceanic Administration, Qingdao, Shandong Province, China
| | - D J Zhang
- School of Marine Science, Ningbo University, Ningbo, Zhejiang Province, China
| | - J Zhou
- School of Marine Science, Ningbo University, Ningbo, Zhejiang Province, China
| | - C D Zhang
- School of Marine Science, Ningbo University, Ningbo, Zhejiang Province, China
| | - X R Su
- School of Marine Science, Ningbo University, Ningbo, Zhejiang Province, China
| | - T W Li
- Ningbo City College of Vocational Technology, Ningbo, Zhejiang Province, China
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Zhu L, Li CH, Su XR, Guo CY, Wang Z, Jin CH, Li Y, Li TW. Identification and assessment of differentially expressed genes involved in growth regulation in Apostichopus japonicus. Genet Mol Res 2013; 12:3028-37. [PMID: 24065658 DOI: 10.4238/2013.august.20.4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Rapid and efficient growth is a major consideration and challenge for global mariculture. The differential growth rate of the sea cucumber, Apostichopus japonicus, has significantly hampered the total production of the industry. In the present study, forward and reverse suppression subtractive hybridization libraries were constructed and sequenced from a fast-growth group and a slow-growth group of the sea cucumber. A total of 142 differentially expressed sequence tags (ESTs) with insertions longer than 150 bp were identified and further analyzed. Fifty-seven of these ESTs (approximately 40%) were functionally annotated for cell structure, energy metabolism, immunity response, and growth factor categories. Six candidate genes, arginine kinase, cytochrome c oxidase subunit I, HSP70, β-actin, ferritin, and the ADP-ribosylation factor, were further validated by quantitative PCR. Significant differences were found between the fast- and slow-growth groups (P < 0.05) for the expression levels of arginine kinase, cytochrome c oxidase, HSP70, the ADP-ribosylation factor, and β-actin. However, no significant difference was observed for ferritin. Our results provide promising candidate gene markers for practical size screening, and also further promote marker-assisted selective breeding of this species.
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Affiliation(s)
- L Zhu
- School of Marine Sciences, Ningbo University, Ningbo, Zhejiang Province, China
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Li CH, Su XR, Wang MQ, Li YY, Li Y, Zhou J, Li TW. Alteration of Phascolosoma esculenta heat shock protein 90 expression under heavy metal exposure and thermal stress. Genet Mol Res 2012; 11:2641-51. [PMID: 22869079 DOI: 10.4238/2012.july.10.14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The full-length complementary DNA (cDNA) of heat shock protein 90 was cloned from Phascolosoma esculenta (PeHSP90) using expressed sequence tag and rapid amplification of cDNA end approaches. The cDNA of PeHSP90 was 2521 bp including a 5'-untranslated region of 110 bp, a 3'-untranslated region of 230 bp, and an open reading frame of 2181 bp. All of the characteristic motifs of the HSP90 family were completely conserved in the deduced amino acid of PeHSP90. The expression of PeHSP90 was induced by 3 heavy metals or elevated temperature, under which Zn²⁺ displayed effects were more toxic than those of Cd²⁺ and Cu²⁺. The polyclonal antibodies generated from the recombinant product of PeHSP90 were specifically identified not only in the recombinant product but also in the native protein from hemocytes. These results strongly suggested that PeHSP90 was involved in heavy metal challenge and thermal stress regulation in P. esculenta.
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Affiliation(s)
- C H Li
- School of Marine Science, Ningbo University, Ningbo, Zhejiang Province, China.
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Gong HM, Xiao S, Su XR, Han JB, Wang QQ. Photochromism and two-photon luminescence of Ag-TiO(2) granular composite films activated by near infrared ps/fs pulses. Opt Express 2007; 15:13924-13929. [PMID: 19550664 DOI: 10.1364/oe.15.013924] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
We reported photochromism and largely enhanced visible two-photon luminescence (TPL) of Ag-TiO(2) granular composite films by using ps/fs laser at the wavelength of 800 nm. Three types of photochromism spectra were observed when the Ag atom fraction are less than, comparable to and larger than the percolation threshold. The strong surface-plasmon-resonance enhanced visible TPL emissions near Ag(2)O transition band from the photoactivated Ag-TiO(2) samples were also observed. Furthermore, we found that the TPL intensity saturatedly increased while the absorbance at 800 nm exponentially decreased with the same rate as the increasing of photoactivation time, which means that both photochromism and TPL of Ag-TiO(2) composite films are originated from the photo-oxidation of Ag to Ag(+). These observations exhibit the multifunctional features of Ag nanoparticle materials.
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