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Chen M, Deng Y, Li Z, Ye Y, Zeng L, He Z, Peng G. SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm. J Cell Mol Med 2024; 28:e18345. [PMID: 38693850 PMCID: PMC11063733 DOI: 10.1111/jcmm.18345] [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: 12/31/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024] Open
Abstract
Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.
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Affiliation(s)
- Min Chen
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yingwei Deng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Zejun Li
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yifan Ye
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Lijun Zeng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Ziyi He
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Guofang Peng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
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2
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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Gao H, Sun J, Wang Y, Lu Y, Liu L, Zhao Q, Shuai J. Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization. Brief Bioinform 2023; 24:bbad259. [PMID: 37466194 DOI: 10.1093/bib/bbad259] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/18/2023] [Accepted: 06/27/2023] [Indexed: 07/20/2023] Open
Abstract
Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.
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Affiliation(s)
- Hongyan Gao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China
| | - Yukun Wang
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Yuer Lu
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Liyu Liu
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Jianwei Shuai
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China
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Peng L, Tan J, Xiong W, Zhang L, Wang Z, Yuan R, Li Z, Chen X. Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data. Comput Biol Med 2023; 163:107137. [PMID: 37364528 DOI: 10.1016/j.compbiomed.2023.107137] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/18/2023] [Accepted: 06/04/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis. METHODS Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors. RESULTS The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells. CONCLUSIONS The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wei Xiong
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Ruya Yuan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
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Chen M, Deng Y, Li Z, Ye Y, He Z. KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection. BMC Bioinformatics 2023; 24:229. [PMID: 37268893 DOI: 10.1186/s12859-023-05365-2] [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: 11/27/2022] [Accepted: 05/26/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments. RESULTS In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. CONCLUSION A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.
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Affiliation(s)
- Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
| | - Yingwei Deng
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China.
| | - Zejun Li
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
| | - Yifan Ye
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
| | - Ziyi He
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
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Wang Z, Gu Y, Zheng S, Yang L, Li J. MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction. Comput Biol Med 2023; 155:106642. [PMID: 36805231 DOI: 10.1016/j.compbiomed.2023.106642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/15/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associations in recent years. However, most of them ignored the combination of abundant genetic, therapeutic information, and gene-disease network topology. To this end, we re-organized the current gene-disease association benchmark dataset by extracting the newest gene-disease associations from the OMIM database. Then, we developed a multi-graph representation learning-based ensemble model, named MGREL to predict gene-disease associations. MGREL integrated two feature generation channels to extract gene and disease features, including a knowledge extraction channel which learned high-order representations from genetic and therapeutic information, and a graph learning channel which acquired network topological representations through multiple advanced graph representation learning methods. Then, an ensemble learning method with 5 machine learning models was used as the classifier to predict the gene-disease association. Comprehensive experiments have demonstrated the significant performance achieved by MGREL compared to 5 state-of-the-art methods. For the major measurements (AUC = 0.925, AUPR = 0.935), the relative improvements of MGREL compared to the suboptimal methods are 3.24%, and 2.75%, respectively. MGREL also achieved impressive improvements in the challenging tasks of predicting potential associations for unknown genes/diseases. In addition, case studies implied potential applications for MGREL in the discovery of potential therapeutic targets.
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Affiliation(s)
- Ziyang Wang
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Yaowen Gu
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Si Zheng
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China; Institute for Artificial Intelligence, Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China
| | - Lin Yang
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Jiao Li
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China.
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Wang FT, Hou YY, Tan X, Huang KJ, Xu J, Cai R. Real-time multiple signal amplification self-powered biosensing platform for ultrasensitive detection of MicroRNA. Biosens Bioelectron 2023; 222:114933. [PMID: 36470063 DOI: 10.1016/j.bios.2022.114933] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022]
Abstract
A real-time self-powered biosensor is designed for ultrasensitive detection of microRNA-21 based on electrochemical energy device capacitor and target-induced recycling double amplification strategy, which greatly improves the output signal by converting a small number of targets into two glucose oxidase labeled output strand DNAs, and the squeezed-out output strand is recycled by the cathode to fix more signal [Ru(NH3)6]3+ to further improve the detection signal. A digital multimeter (DMM) is connected to computer for real-time displaying the output signal of the self-powered biosensing system, which improves the accuracy of the sensing platform. The sensitivity of the proposed biosensor is 116.15 μA/pM for target microRNA-21, which is 32.26 times higher than that of pure EBFC (3.6 μA/pM). The target concentration is proportional to the open-circuit voltage value in a wide linear range of 0.1-10000 fM with a low detection limit of 0.04 fM (S/N = 3). The method shows high sensitivity and excellent selectivity, and can be applied to detect tumor marker microRNA-21 in biological matrix.
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Affiliation(s)
- Fu-Ting Wang
- Molecular Science and Biomedicine Laboratory, State Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Material Science and Engineering, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha, 410082, China
| | - Yang-Yang Hou
- College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang, 464000, China
| | - Xuecai Tan
- School of Chemistry and Chemical Engineering, Guangxi Minzu University; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products; Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Products; Key Laboratory of Guangxi Colleges and Universities for Food Safety and Pharmaceutical Analytical Chemistry. Nanning 530008, China
| | - Ke-Jing Huang
- School of Chemistry and Chemical Engineering, Guangxi Minzu University; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products; Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Products; Key Laboratory of Guangxi Colleges and Universities for Food Safety and Pharmaceutical Analytical Chemistry. Nanning 530008, China.
| | - Jing Xu
- College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang, 464000, China.
| | - Ren Cai
- Molecular Science and Biomedicine Laboratory, State Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Material Science and Engineering, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha, 410082, China.
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SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder. Cells 2022; 11:cells11243984. [PMID: 36552748 PMCID: PMC9776508 DOI: 10.3390/cells11243984] [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: 11/14/2022] [Revised: 11/30/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.
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Song JJ, Li H, Wang N, Zhou XY, Liu Y, Zhang Z, Feng Q, Chen YL, Liu D, Liang J, Ma XY, Wen XR, Fu YY. Gastrodin ameliorates the lipopolysaccharide-induced neuroinflammation in mice by downregulating miR-107-3p. Front Pharmacol 2022; 13:1044375. [PMID: 36569291 PMCID: PMC9773390 DOI: 10.3389/fphar.2022.1044375] [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: 09/14/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Background: Neuroinflammation plays a pivotal role in the pathogenesis of Central Nervous System (CNS) diseases. The phenolic glucoside gastrodin (GAS), has been known to treat CNS disorders by exerting anti-inflammatory activities. Our aim was to investigate the potential neuroprotective mechanisms of GAS on lipopolysaccharide (LPS)-induced mice. Methods: Male C57BL/6J mice were treated by LPS, before which GAS was adminisrated. The behavior tests such as forced swim test, tail suspension test, and elevated plus maze were performed to evaluate depressive-anxiety-like behaviors. A high-throughput sequencing (HTS) analysis was performed to screen out distinctive miRNAs which were validated using quantitative real-time PCR. Then, miRNA agomir or NC was injected stereotaxically into hippocampus of mice to explore the role of miRNA on GAS in response to LPS. Furthermore, Immunofluorescence and the hematoxylin and eosin (H&E) staining were employed to observe the cellular morphology. The protein levels of pro-inflammatory factors were evaluated by western blot. Finally, the target mRNA of miRNA was predicted using bioinformatics analysis. GO and KEGG enrichment analyses were conducted to clarify the potential function of target protein, which were visualized by bubble charts. Results: The behavioral data showed that mice in the LPS group had obvious depressive-anxiety-like behaviors, and 100 mg/kg GAS could improve these behavioral changes and alleviate the levels of pro-inflammatory cytokines in the hippocampus when mice were exposed to LPS for 6 h. Meanwhile, LPS-induced microglia and astrocyte activation in the CA1, CA2, CA3, and DG regions of the hippocampus were also reversed by GAS. Furthermore, miR-107-3p were screened out and verified for GAS in response to LPS. Importantly, miR-107-3p overexpression negatively abrogated the neuroprotective effects of GAS. Moreover, KPNA1 might be the target molecular of miR-107-3p. KPNA1 might regulate 12 neuroinflammation-related genes, which were mainly involved in cytokine-mediated signaling pathway. Conclusion: These results suggested that GAS might alleviate the LPS-induced neuroinflammation and depressive-anxiety-like behaviors in mice by downregulating miR-107-3p and upregulating the downstream target KPNA1. The indicates miR-107-3p may provide a new strategy for the treatment of CNS diseases.
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Affiliation(s)
- Jin-Jin Song
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China,Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
| | - Hui Li
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Nan Wang
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
| | - Xiao-Yan Zhou
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China,Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
| | - Yan Liu
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China,Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
| | - Zhen Zhang
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Qian Feng
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China,Clinical Laboratory, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yu-Ling Chen
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dan Liu
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jia Liang
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiang-Yu Ma
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiang-Ru Wen
- College of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, China,*Correspondence: Xiang-Ru Wen, ; Yan-Yan Fu,
| | - Yan-Yan Fu
- Department of Genetics, Key Laboratory of Genetic Foundation and Clinical Application, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China,Department of Cell Biology and Neurobiology, Xuzhou Medical University, Xuzhou, Jiangsu, China,*Correspondence: Xiang-Ru Wen, ; Yan-Yan Fu,
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10
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Zhang J, Chen Z, Chen H, Deng Y, Li S, Jin L. Recent Advances in the Roles of MicroRNA and MicroRNA-Based Diagnosis in Neurodegenerative Diseases. BIOSENSORS 2022; 12:1074. [PMID: 36551041 PMCID: PMC9776063 DOI: 10.3390/bios12121074] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/17/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases manifest as progressive loss of neuronal structures and their myelin sheaths and lead to substantial morbidity and mortality, especially in the elderly. Despite extensive research, there are few effective treatment options for the diseases. MicroRNAs have been shown to be involved in the developmental processes of the central nervous system. Mounting evidence suggest they play an important role in the development of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. However, there are few reviews regarding the roles of miRNAs in neurodegenerative diseases. This review summarizes the recent developments in the roles of microRNAs in neurodegenerative diseases and presents the application of microRNA-based methods in the early diagnosis of these diseases.
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11
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Chen L, Lin D, Xu H, Li J, Lin L. WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19. Front Microbiol 2022; 13:1040252. [PMID: 36466666 PMCID: PMC9713947 DOI: 10.3389/fmicb.2022.1040252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/06/2022] [Indexed: 11/18/2022] Open
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV) has led to a huge health and economic crises. However, the research required to develop new drugs and vaccines is very expensive in terms of labor, money, and time. Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. Using the previously reported human drug virus database (HDVD), we proposed a model to predict possible drug regimens based on a weighted reconstruction-based linear label propagation algorithm (WLLP). For the drug–virus association matrix, we used the weighted K-nearest known neighbors method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses to obtain the final prediction results. In the framework of 10 times 10-fold cross-validated area under the receiver operating characteristic (ROC) curve (AUC), WLLP exhibited excellent performance with an AUC of 0.8828 ± 0.0037 and an area under the precision-recall curve of 0.5277 ± 0.0053, outperforming the other four models used for comparison. We also predicted effective drug regimens against SARS-CoV-2, and this case study showed that WLLP can be used to suggest potential drugs for the treatment of COVID-19.
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Affiliation(s)
- Langcheng Chen
- Center of Campus Network and Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
| | - Dongying Lin
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Haojie Xu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Jianming Li
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Lieqing Lin
- Center of Campus Network and Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
- *Correspondence: Lieqing Lin
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12
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Cao B, Li R, Xiao S, Deng S, Zhou X, Zhou L. Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE. iScience 2022; 25:105299. [DOI: 10.1016/j.isci.2022.105299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/08/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
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13
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Xu D, Di K, Fan B, Wu J, Gu X, Sun Y, Khan A, Li P, Li Z. MicroRNAs in extracellular vesicles: Sorting mechanisms, diagnostic value, isolation, and detection technology. Front Bioeng Biotechnol 2022; 10:948959. [PMID: 36324901 PMCID: PMC9618890 DOI: 10.3389/fbioe.2022.948959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
MicroRNAs (miRNAs) are a class of short, single-stranded, noncoding RNAs, with a length of about 18–22 nucleotides. Extracellular vesicles (EVs) are derived from cells and play a vital role in the development of diseases and can be used as biomarkers for liquid biopsy, as they are the carriers of miRNA. Existing studies have found that most of the functions of miRNA are mainly realized through intercellular transmission of EVs, which can protect and sort miRNAs. Meanwhile, detection sensitivity and specificity of EV-derived miRNA are higher than those of conventional serum biomarkers. In recent years, EVs have been expected to become a new marker for liquid biopsy. This review summarizes recent progress in several aspects of EVs, including sorting mechanisms, diagnostic value, and technology for isolation of EVs and detection of EV-derived miRNAs. In addition, the study reviews challenges and future research avenues in the field of EVs, providing a basis for the application of EV-derived miRNAs as a disease marker to be used in clinical diagnosis and even for the development of point-of-care testing (POCT) platforms.
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Affiliation(s)
- Dongjie Xu
- College of Animal Science, Yangtze University, Jingzhou, China
| | - Kaili Di
- Department of Laboratory Medicine, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Boyue Fan
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Jie Wu
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Xinrui Gu
- Department of Laboratory Medicine, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yifan Sun
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Adeel Khan
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, National Demonstration Center for Experimental Biomedical Engineering Education (Southeast University), Southeast University, Nanjing, China
| | - Peng Li
- College of Animal Science, Yangtze University, Jingzhou, China
- *Correspondence: Peng Li, ; Zhiyang Li,
| | - Zhiyang Li
- Department of Laboratory Medicine, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- *Correspondence: Peng Li, ; Zhiyang Li,
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14
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Xie G, Xu H, Li J, Gu G, Sun Y, Lin Z, Zhu Y, Wang W, Wang Y, Shao J. DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19. Comput Chem Eng 2022; 166:107947. [PMID: 35942213 PMCID: PMC9349049 DOI: 10.1016/j.compchemeng.2022.107947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 04/13/2022] [Accepted: 07/27/2022] [Indexed: 12/25/2022]
Abstract
Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Haojie Xu
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Jianming Li
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Guosheng Gu
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China,Corresponding author
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhiyi Lin
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Yinting Zhu
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Weiming Wang
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Youfu Wang
- Huaneng Qinghai Power Generation Co., Ltd. New Energy Branch, Xining 810000, China
| | - Jiang Shao
- School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China
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15
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Huang R, He L, Jin L, Li Z, He N, Miao W. Recent advancements in DNA nanotechnology-enabled extracellular vesicles detection and diagnosis: A mini review. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.107926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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16
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Zhai S, Li X, Wu Y, Shi X, Ji B, Qiu C. Identifying potential microRNA biomarkers for colon cancer and colorectal cancer through bound nuclear norm regularization. Front Genet 2022; 13:980437. [PMID: 36313468 PMCID: PMC9614659 DOI: 10.3389/fgene.2022.980437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022] Open
Abstract
Colon cancer and colorectal cancer are two common cancer-related deaths worldwide. Identification of potential biomarkers for the two cancers can help us to evaluate their initiation, progression and therapeutic response. In this study, we propose a new microRNA-disease association identification method, BNNRMDA, to discover potential microRNA biomarkers for the two cancers. BNNRMDA better combines disease semantic similarity and Gaussian Association Profile Kernel (GAPK) similarity, microRNA function similarity and GAPK similarity, and the bound nuclear norm regularization model. Compared to other five classical microRNA-disease association identification methods (MIDPE, MIDP, RLSMDA, GRNMF, AND LPLNS), BNNRMDA obtains the highest AUC of 0.9071, demonstrating its strong microRNA-disease association identification performance. BNNRMDA is applied to discover possible microRNA biomarkers for colon cancer and colorectal cancer. The results show that all 73 known microRNAs associated with colon cancer in the HMDD database have the highest association scores with colon cancer and are ranked as top 73. Among 137 known microRNAs associated with colorectal cancer in the HMDD database, 129 microRNAs have the highest association scores with colorectal cancer and are ranked as top 129. In addition, we predict that hsa-miR-103a could be a potential biomarker of colon cancer and hsa-mir-193b and hsa-mir-7days could be potential biomarkers of colorectal cancer.
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Affiliation(s)
- Shengyong Zhai
- Department of General Surgery, Weifang People’s Hospital, Shandong, China
| | - Xiaoling Li
- The Second Department of Oncology, Beidahuang Industry Group General Hospital, Harbin, China,Heilongjiang Second Cancer Hospital, Harbin, China
| | - Yan Wu
- Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoli Shi
- Geneis Beijing Co., Ltd., Beijing, China
| | - Binbin Ji
- Geneis Beijing Co., Ltd., Beijing, China
| | - Chun Qiu
- Department of Oncology, Hainan General Hospital, Haikou, China,*Correspondence: Chun Qiu,
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17
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Liu S, Zhao K, Huang M, Zeng M, Deng Y, Li S, Chen H, Li W, Chen Z. Research progress on detection techniques for point-of-care testing of foodborne pathogens. Front Bioeng Biotechnol 2022; 10:958134. [PMID: 36003541 PMCID: PMC9393618 DOI: 10.3389/fbioe.2022.958134] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 06/30/2022] [Indexed: 11/21/2022] Open
Abstract
The global burden of foodborne disease is enormous and foodborne pathogens are the leading cause of human illnesses. The detection of foodborne pathogenic bacteria has become a research hotspot in recent years. Rapid detection methods based on immunoassay, molecular biology, microfluidic chip, metabolism, biosensor, and mass spectrometry have developed rapidly and become the main methods for the detection of foodborne pathogens. This study reviewed a variety of rapid detection methods in recent years. The research advances are introduced based on the above technical methods for the rapid detection of foodborne pathogenic bacteria. The study also discusses the limitations of existing methods and their advantages and future development direction, to form an overall understanding of the detection methods, and for point-of-care testing (POCT) applications to accurately and rapidly diagnose and control diseases.
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Affiliation(s)
- Sha Liu
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Kaixuan Zhao
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Meiyuan Huang
- Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Department of Pathology, Central South University, Zhuzhou, China
| | - Meimei Zeng
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Yan Deng
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Song Li
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Hui Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Wen Li
- College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Zhu Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
- *Correspondence: Zhu Chen,
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18
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Sun F, Sun J, Zhao Q. A deep learning method for predicting metabolite-disease associations via graph neural network. Brief Bioinform 2022; 23:6640005. [PMID: 35817399 DOI: 10.1093/bib/bbac266] [Citation(s) in RCA: 111] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/04/2022] [Accepted: 06/06/2022] [Indexed: 12/15/2022] Open
Abstract
Metabolism is the process by which an organism continuously replaces old substances with new substances. It plays an important role in maintaining human life, body growth and reproduction. More and more researchers have shown that the concentrations of some metabolites in patients are different from those in healthy people. Traditional biological experiments can test some hypotheses and verify their relationships but usually take a considerable amount of time and money. Therefore, it is urgent to develop a new computational method to identify the relationships between metabolites and diseases. In this work, we present a new deep learning algorithm named as graph convolutional network with graph attention network (GCNAT) to predict the potential associations of disease-related metabolites. First, we construct a heterogeneous network based on known metabolite-disease associations, metabolite-metabolite similarities and disease-disease similarities. Metabolite and disease features are encoded and learned through the graph convolutional neural network. Then, a graph attention layer is used to combine the embeddings of multiple convolutional layers, and the corresponding attention coefficients are calculated to assign different weights to the embeddings of each layer. Further, the prediction result is obtained by decoding and scoring the final synthetic embeddings. Finally, GCNAT achieves a reliable area under the receiver operating characteristic curve of 0.95 and the precision-recall curve of 0.405, which are better than the results of existing five state-of-the-art predictive methods in 5-fold cross-validation, and the case studies show that the metabolite-disease correlations predicted by our method can be successfully demonstrated by relevant experiments. We hope that GCNAT could be a useful biomedical research tool for predicting potential metabolite-disease associations in the future.
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Affiliation(s)
- Feiyue Sun
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
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19
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Liu W, Lin H, Huang L, Peng L, Tang T, Zhao Q, Yang L. Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder. Brief Bioinform 2022; 23:6553934. [PMID: 35325038 DOI: 10.1093/bib/bbac104] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/18/2022] [Accepted: 03/01/2022] [Indexed: 12/31/2022] Open
Abstract
Increasing evidences show that the occurrence of human complex diseases is closely related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease-related miRNAs is essential for the diagnosis and treatment of complex human diseases. Although some current computational methods can effectively predict potential disease-related miRNAs, the accuracy of prediction should be further improved. In our study, a new computational method via deep forest ensemble learning based on autoencoder (DFELMDA) is proposed to predict miRNA-disease associations. Specifically, a new feature representation strategy is proposed to obtain different types of feature representations (from miRNA and disease) for each miRNA-disease association. Then, two types of low-dimensional feature representations are extracted by two deep autoencoders for predicting miRNA-disease associations. Finally, two prediction scores of the miRNA-disease associations are obtained by the deep random forest and combined to determine the final results. DFELMDA is compared with several classical methods on the The Human microRNA Disease Database (HMDD) dataset. Results reveal that the performance of this method is superior. The area under receiver operating characteristic curve (AUC) values obtained by DFELMDA through 5-fold and 10-fold cross-validation are 0.9552 and 0.9560, respectively. In addition, case studies on colon, breast and lung tumors of different disease types further demonstrate the excellent ability of DFELMDA to predict disease-associated miRNA-disease. Performance analysis shows that DFELMDA can be used as an effective computational tool for predicting miRNA-disease associations.
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Affiliation(s)
- Wei Liu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Hui Lin
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Ting Tang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Li Yang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
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20
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Zhang HY, Wang L, You ZH, Hu L, Zhao BW, Li ZW, Li YM. iGRLCDA: identifying circRNA-disease association based on graph representation learning. Brief Bioinform 2022; 23:6552271. [PMID: 35323894 DOI: 10.1093/bib/bbac083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/18/2022] Open
Abstract
While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.
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Affiliation(s)
- Han-Yuan Zhang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China.,College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zheng-Wei Li
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
| | - Yang-Ming Li
- College of Engineering Technology, Rochester Institute of Technology, Rochester, NY 14623, USA
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21
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Li G, Wang D, Zhang Y, Liang C, Xiao Q, Luo J. Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data. Front Genet 2022; 13:829937. [PMID: 35198012 PMCID: PMC8859418 DOI: 10.3389/fgene.2022.829937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA-disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA-disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA-disease pairs.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Diancheng Wang
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Yuejin Zhang
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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22
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Lang J, Zhu R, Sun X, Zhu S, Li T, Shi X, Sun Y, Yang Z, Wang W, Bing P, He B, Tian G. Evaluation of the MGISEQ-2000 Sequencing Platform for Illumina Target Capture Sequencing Libraries. Front Genet 2021; 12:730519. [PMID: 34777467 PMCID: PMC8578046 DOI: 10.3389/fgene.2021.730519] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/24/2021] [Indexed: 01/19/2023] Open
Abstract
Illumina is the leading sequencing platform in the next-generation sequencing (NGS) market globally. In recent years, MGI Tech has presented a series of new sequencers, including DNBSEQ-T7, MGISEQ-2000 and MGISEQ-200. As a complex application of NGS, cancer-detecting panels pose increasing demands for the high accuracy and sensitivity of sequencing and data analysis. In this study, we used the same capture DNA libraries constructed based on the Illumina protocol to evaluate the performance of the Illumina Nextseq500 and MGISEQ-2000 sequencing platforms. We found that the two platforms had high consistency in the results of hotspot mutation analysis; more importantly, we found that there was a significant loss of fragments in the 101-133 bp size range on the MGISEQ-2000 sequencing platform for Illumina libraries, but not for the capture DNA libraries prepared based on the MGISEQ protocol. This phenomenon may indicate fragment selection or low fragment ligation efficiency during the DNA circularization step, which is a unique step of the MGISEQ-2000 sequence platform. In conclusion, these different sequencing libraries and corresponding sequencing platforms are compatible with each other, but protocol and platform selection need to be carefully evaluated in combination with research purpose.
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Affiliation(s)
- Jidong Lang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Academician Workstation, Changsha Medical University, Changsha, China
| | - Rongrong Zhu
- Vascular Surgery Department, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Xue Sun
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Siyu Zhu
- Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA, United States
| | - Tianbao Li
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoli Shi
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Yanqi Sun
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Zhou Yang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Weiwei Wang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Geng Tian
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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23
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Shen Z, Liu T, Xu T. Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5770981. [PMID: 34413898 PMCID: PMC8369162 DOI: 10.1155/2021/5770981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 01/19/2023]
Abstract
Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.
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Affiliation(s)
- Zhehan Shen
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Taigang Liu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Ting Xu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
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Chen XJ, Hua XY, Jiang ZR. ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations. BMC Bioinformatics 2021; 22:358. [PMID: 34215183 PMCID: PMC8254275 DOI: 10.1186/s12859-021-04266-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 06/11/2021] [Indexed: 11/24/2022] Open
Abstract
Background A growing proportion of research has proved that microRNAs (miRNAs) can regulate the function of target genes and have close relations with various diseases. Developing computational methods to exploit more potential miRNA-disease associations can provide clues for further functional research. Results Inspired by the work of predecessors, we discover that the noise hiding in the data can affect the prediction performance and then propose an anti-noise algorithm (ANMDA) to predict potential miRNA-disease associations. Firstly, we calculate the similarity in miRNAs and diseases to construct features and obtain positive samples according to the Human MicroRNA Disease Database version 2.0 (HMDD v2.0). Then, we apply k-means on the undetected miRNA-disease associations and sample the negative examples equally from the k-cluster. Further, we construct several data subsets through sampling with replacement to feed on the light gradient boosting machine (LightGBM) method. Finally, the voting method is applied to predict potential miRNA-disease relationships. As a result, ANMDA can achieve an area under the receiver operating characteristic curve (AUROC) of 0.9373 ± 0.0005 in five-fold cross-validation, which is superior to several published methods. In addition, we analyze the predicted miRNA-disease associations with high probability and compare them with the data in HMDD v3.0 in the case study. The results show ANMDA is a novel and practical algorithm that can be used to infer potential miRNA-disease associations. Conclusion The results indicate the noise hiding in the data has an obvious impact on predicting potential miRNA-disease associations. We believe ANMDA can achieve better results from this task with more methods used in dealing with the data noise. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04266-6.
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Affiliation(s)
- Xue-Jun Chen
- School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
| | - Xin-Yun Hua
- School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
| | - Zhen-Ran Jiang
- School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China.
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25
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Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021; 22:6261915. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Xuhong Wang
- School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, China
| | - Qiuying Dai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, China
| | | | | | - Dennis Russell Salahub
- Department of Chemistry, University of Calgary, Fellow Royal Society of Canada and Fellow of the American Association for the Advancement of Science, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
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26
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Chen Y, Sun H, Sun M, Shi C, Sun H, Shi X, Ji B, Cui J. Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe-Disease Association Prediction. Front Microbiol 2021; 12:650056. [PMID: 33796094 PMCID: PMC8007907 DOI: 10.3389/fmicb.2021.650056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/09/2021] [Indexed: 12/02/2022] Open
Abstract
Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe-disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe-disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe-disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.
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Affiliation(s)
- Yu Chen
- The Cancer Hospital of Jia Mu Si, Jiamusi, China
| | - Hongjian Sun
- Oncological Surgery, The Central Hospital of Jia Mu Si, Jiamusi, China
| | - Mengzhe Sun
- Oncological Surgery, The Central Hospital of Jia Mu Si, Jiamusi, China
| | - Changguo Shi
- Department of Thoracic Surgery, The Cancer Hospital of Jia Mu Si, Jiamusi, China
| | - Hongmei Sun
- Medical Oncology, The Cancer Hospital of Jia Mu Si, Jiamusi, China
| | - Xiaoli Shi
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Binbin Ji
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jinpeng Cui
- Department of Laboratory Medicine, Yantaishan Hospital of Yantai City, Yantai, China
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Meng Y, Jin M, Tang X, Xu J. Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study. Appl Soft Comput 2021; 103:107135. [PMID: 33519322 PMCID: PMC7825831 DOI: 10.1016/j.asoc.2021.107135] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/10/2021] [Accepted: 01/20/2021] [Indexed: 12/21/2022]
Abstract
The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug–virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug–virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug–virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug–virus network, which integrates the known drug–virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug–virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug–virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF.
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Affiliation(s)
- Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Min Jin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Xianfang Tang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
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28
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Peng L, Tian X, Shen L, Kuang M, Li T, Tian G, Yang J, Zhou L. Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction. Front Genet 2020; 11:577387. [PMID: 33193695 PMCID: PMC7525008 DOI: 10.3389/fgene.2020.577387] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 08/18/2020] [Indexed: 12/12/2022] Open
Abstract
A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xiongfei Tian
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ming Kuang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Tianbao Li
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China
| | | | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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29
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Zhang Y, Chen M, Cheng X, Wei H. MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection. Front Genet 2020; 11:389. [PMID: 32425980 PMCID: PMC7204399 DOI: 10.3389/fgene.2020.00389] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 03/27/2020] [Indexed: 12/11/2022] Open
Abstract
Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.
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Affiliation(s)
- Yi Zhang
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Xiaohui Cheng
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Hanyan Wei
- School of Pharmacy, Guilin Medical University, Guilin, China
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