1
|
Bing P, Liu W, Zhai Z, Li J, Guo Z, Xiang Y, He B, Zhu L. A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Front Cardiovasc Med 2024; 11:1277123. [PMID: 38699582 PMCID: PMC11064874 DOI: 10.3389/fcvm.2024.1277123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/25/2024] [Indexed: 05/05/2024] Open
Abstract
Background Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. Methods In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity. Results The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD). Conclusions The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.
Collapse
Affiliation(s)
- Pingping Bing
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Wei Liu
- College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Zhixing Zhai
- College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Jianghao Li
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Zhiqun Guo
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Yanrui Xiang
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Binsheng He
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Lemei Zhu
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| |
Collapse
|
2
|
Zhang Y, Chu Y, Lin S, Xiong Y, Wei DQ. ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler. Brief Bioinform 2024; 25:bbae103. [PMID: 38517693 PMCID: PMC10959163 DOI: 10.1093/bib/bbae103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/04/2024] [Accepted: 02/23/2024] [Indexed: 03/24/2024] Open
Abstract
Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.
Collapse
Affiliation(s)
- Yufang Zhang
- School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
- Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, China
| | - Yanyi Chu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
| | - Dong-Qing Wei
- Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, China
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
3
|
Pan P, Li J, Wang B, Tan X, Yin H, Han Y, Wang H, Shi X, Li X, Xie C, Chen L, Chen L, Bai Y, Li Z, Tian G. Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis. Front Oncol 2023; 13:1067849. [PMID: 37546388 PMCID: PMC10401844 DOI: 10.3389/fonc.2023.1067849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/21/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain. Methods In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data. Results Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice. Conclusion This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.
Collapse
Affiliation(s)
- Peng Pan
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Jingnan Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Beijing, China
| | - Bo Wang
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoyan Tan
- Department of Gastroenterology, Maoming People's Hospital, Maoming, China
| | - Hekun Yin
- Department of Gastroenterology, Jiangmen Central Hospital, Jiangmen, China
| | - Yingmin Han
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Haobin Wang
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Xiaoli Shi
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoshuang Li
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Cuinan Xie
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Longfei Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Lanyou Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Yu Bai
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Zhaoshen Li
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Geng Tian
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| |
Collapse
|
4
|
Guo Z, Li Z, Zhang M, Bao M, He B, Zhou X. LncRNA FAS-AS1 upregulated by its genetic variation rs6586163 promotes cell apoptosis in nasopharyngeal carcinoma through regulating mitochondria function and Fas splicing. Sci Rep 2023; 13:8218. [PMID: 37217794 DOI: 10.1038/s41598-023-35502-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/18/2023] [Indexed: 05/24/2023] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a common head and neck malignant with a high incidence in Southern China. Genetic aberrations play a vital role in the pathogenesis, progression and prognosis of NPC. In the present study, we elucidated the underlying mechanism of FAS-AS1 and its genetic variation rs6586163 in NPC. We demonstrated that FAS-AS1 rs6586163 variant genotype carriers were associated with lower risk of NPC (CC vs. AA, OR = 0.645, P = 0.006) and better overall survival (AC + CC vs. AA, HR = 0.667, P = 0.030). Mechanically, rs6586163 increased the transcriptional activity of FAS-AS1 and contributed to ectopic overexpression of FAS-AS1 in NPC. rs6586163 also exhibited an eQTL trait and the genes affected by rs6586163 were enriched in apoptosis related signaling pathway. FAS-AS1 was downregulated in NPC tissues and over-expression of FAS-AS1 was associated with early clinical stage and better short-term treatment efficacy for NPC patients. Overexpression of FAS-AS1 inhibited NPC cell viability and promoted cell apoptosis. GSEA analysis of RNA-seq data suggested FAS-AS1 participate in mitochondria regulation and mRNA alternative splicing. Transmission electron microscopic examination verified that the mitochondria was swelled, the mitochondrial cristae was fragmented or disappeared, and their structures were destroyed in FAS-AS1 overexpressed cells. Furthermore, we identified HSP90AA1, CS, BCL2L1, SOD2 and PPARGC1A as the top 5 hub genes of FAS-AS1 regulated genes involved in mitochondria function. We also proved FAS-AS1 could affect Fas splicing isoform sFas/mFas expression ratio, and apoptotic protein expression, thus leading to increased apoptosis. Our study provided the first evidence that FAS-AS1 and its genetic polymorphism rs6586163 triggered apoptosis in NPC, which might have a potential as new biomarkers for NPC susceptibility and prognosis.
Collapse
Affiliation(s)
- Zhen Guo
- Academician Workstation, Changsha Medical University, LeiFeng Avenue No.1501, Changsha, 410219, People's Republic of China
- Hunan Key Laboratory of the Fundamental and Clinical Research on Functional Nucleic Acid, Changsha Medical University, Changsha, 410219, People's Republic of China
- Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, People's Republic of China
| | - ZiBo Li
- Academician Workstation, Changsha Medical University, LeiFeng Avenue No.1501, Changsha, 410219, People's Republic of China
| | - MengLing Zhang
- School of Stomatology, Changsha Medical University, Changsha, 410219, People's Republic of China
| | - MeiHua Bao
- Academician Workstation, Changsha Medical University, LeiFeng Avenue No.1501, Changsha, 410219, People's Republic of China
| | - BinSheng He
- Academician Workstation, Changsha Medical University, LeiFeng Avenue No.1501, Changsha, 410219, People's Republic of China
| | - XiaoLong Zhou
- Academician Workstation, Changsha Medical University, LeiFeng Avenue No.1501, Changsha, 410219, People's Republic of China.
- Hunan Key Laboratory of the Fundamental and Clinical Research on Functional Nucleic Acid, Changsha Medical University, Changsha, 410219, People's Republic of China.
- Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, People's Republic of China.
| |
Collapse
|
5
|
Huang Z, Gao Y, Han Y, Yang J, Yang C, Li S, Zhou D, Huang Q, Yang J. Revealing the roles of TLR7, a nucleic acid sensor for COVID-19 in pan-cancer. BIOSAFETY AND HEALTH 2023:S2590-0536(23)00054-X. [PMID: 37362864 PMCID: PMC10167782 DOI: 10.1016/j.bsheal.2023.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 06/28/2023] Open
Abstract
Recent studies suggested that cancer was a risk factor for coronavirus disease 2019 (COVID-19). Toll-like receptor 7 (TLR7), a severe acute respiratory syndrome 2 (SARS-CoV-2) virus's nucleic acid sensor, was discovered to be aberrantly expressed in many types of cancers. However, its expression pattern across cancers and association with COVID-19 (or its causing virus SARS-CoV-2) has not been systematically studied. In this study, we proposed a computational framework to comprehensively study the roles of TLR7 in COVID-19 and pan-cancers at genetic, gene expression, protein, epigenetic, and single-cell levels. We applied the computational framework in a few databases, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), lung gene expression data of mice infected with SARS-CoV-2, and the like. As a result, TLR7 expression was found to be higher in the lung of mice infected with SARS-CoV-2 than that in the control group. The analysis in the Opentargets database also confirmed the association between TLR7 and COVID-19. There are also a few exciting findings in cancers. First, the most common type of TLR7 was "Missense" at the genomic level. Second, TLR7 mRNA expression was significantly up-regulated in 6 cancer types and down-regulated in 6 cancer types compared to normal tissues, further validated in the HPA database at the protein level. The genes significantly co-expressed with TLR7 were mainly enriched in the toll-like receptor signaling pathway, endolysosome, and signaling pattern recognition receptor activity. In addition, the abnormal TLR7 expression was associated with mismatch repair (MMR), microsatellite instability (MSI), and tumor mutational burden (TMB) in various cancers. Mined by the ESTIMATE algorithm, the expression of TLR7 was also closely linked to various immune infiltration patterns in pan-cancer, and TLR7 was mainly enriched in macrophages, as revealed by single-cell RNA sequencing. Third, abnormal expression of TLR7 could predict the survival of Brain Lower Grade Glioma (LGG), Lung adenocarcinoma (LUAD), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), and Testicular Germ Cell Tumors (TGCT) patients, respectively. Finally, TLR7 expressions were very sensitive to a few targeted drugs, such as Alectinib and Imiquimod. In conclusion, TLR7 might be essential in the pathogenesis of COVID-19 and cancers.
Collapse
Affiliation(s)
- Zhijian Huang
- Department of Breast Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Yaoxin Gao
- Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yuanyuan Han
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming 650000, China
| | - Jingwen Yang
- Department of Clinical Pharmacy, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Can Yang
- Department of Breast Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Shixiong Li
- Department of Breast Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Decong Zhou
- Geriatric Hospital of Hainan Medical Education Department, Haikou 571100, China
| | - Qiuyan Huang
- Department of Breast Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Jialiang Yang
- Geneis Beijing Co., Ltd, Beijing 100102, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| |
Collapse
|
6
|
Zhang L, Lu D, Bi X, Zhao K, Yu G, Quan N. Predicting disease genes based on multi-head attention fusion. BMC Bioinformatics 2023; 24:162. [PMID: 37085750 PMCID: PMC10122338 DOI: 10.1186/s12859-023-05285-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/12/2023] [Indexed: 04/23/2023] Open
Abstract
BACKGROUND The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. RESULTS This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. CONCLUSIONS The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.
Collapse
Affiliation(s)
- Linlin Zhang
- College of Software Engineering, Xinjiang University, Urumqi, China.
| | - Dianrong Lu
- College of information Science and Engineering, Xinjiang University, Urumqi, China
| | - Xuehua Bi
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi, China
| | - Kai Zhao
- College of information Science and Engineering, Xinjiang University, Urumqi, China
| | - Guanglei Yu
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi, China
| | - Na Quan
- College of information Science and Engineering, Xinjiang University, Urumqi, China
| |
Collapse
|
7
|
Liu H, Bing P, Zhang M, Tian G, Ma J, Li H, Bao M, He K, He J, He B, Yang J. MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm. Comput Struct Biotechnol J 2023; 21:1414-1423. [PMID: 36824227 PMCID: PMC9941872 DOI: 10.1016/j.csbj.2022.12.053] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023] Open
Abstract
Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. Availability The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.
Collapse
Affiliation(s)
- Haiyan Liu
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,College of Information Engineering, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China
| | - Meijun Zhang
- Geneis Beijing Co., Ltd., Beijing 100102, PR China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing 100102, PR China
| | - Jun Ma
- College of Information Engineering, Changsha Medical University, Changsha 410219, PR China
| | - Haigang Li
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Meihua Bao
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Kunhui He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| | - Jialiang Yang
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,Geneis Beijing Co., Ltd., Beijing 100102, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| |
Collapse
|
8
|
Wang Y, Xiang J, Liu C, Tang M, Hou R, Bao M, Tian G, He J, He B. Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization. Front Microbiol 2022; 13:1062281. [PMID: 36545200 PMCID: PMC9762482 DOI: 10.3389/fmicb.2022.1062281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.
Collapse
Affiliation(s)
- Yibai Wang
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Ju Xiang
- School of Information Engineering, Changsha Medical University, Changsha, China,Academician Workstation, Changsha Medical University, Changsha, China,*Correspondence: Ju Xiang,
| | - Cuicui Liu
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Rui Hou
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Meihua Bao
- School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Jianjun He,
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Binsheng He,
| |
Collapse
|
9
|
Li S, Yang M, Ji L, Fan H. A multi-omics machine learning framework in predicting the recurrence and metastasis of patients with pancreatic adenocarcinoma. Front Microbiol 2022; 13:1032623. [PMID: 36406449 PMCID: PMC9669652 DOI: 10.3389/fmicb.2022.1032623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/17/2022] [Indexed: 10/15/2023] Open
Abstract
Local recurrence and distant metastasis are the main causes of death in patients with pancreatic adenocarcinoma (PDAC). Microbial content in PDAC metastasis is still not well-characterized. Here, the tissue microbiome was comprehensively compared between metastatic and non-metastatic PDAC patients. We found that the pancreatic tissue microbiome of metastatic patients was significantly different from that of non-metastatic patients. Further, 10 potential bacterial biomarkers (Kurthia, Gulbenkiania, Acetobacterium and Planctomyces etc.) were identified by differential analysis. Meanwhile, significant differences in expression patterns across multiple omics (lncRNA, miRNA, and mRNA) of PDAC patients were found. The highest accuracy was achieved when these 10 bacterial biomarkers were used as features to predict recurrence or metastasis in PDAC patients, with an AUC of 0.815. Finally, the recurrence and metastasis in PDAC patients were associated with reduced survival and this association was potentially driven by the 10 biomarkers we identified. Our studies highlight the association between the tissue microbiome and recurrence or metastasis of pancreatic adenocarcioma patients, as well as the survival of patients.
Collapse
Affiliation(s)
- Shenming Li
- Department of Hepatobiliary and Pancreaticosplenic Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Nephrology, Essen University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Min Yang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, Anhui, China
- Genesis Beijing Co., Ltd., Beijing, China
| | - Lei Ji
- Genesis Beijing Co., Ltd., Beijing, China
| | - Hua Fan
- Department of Hepatobiliary and Pancreaticosplenic Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| |
Collapse
|