151
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Wei L, Chen H, Su R. M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning. MOLECULAR THERAPY-NUCLEIC ACIDS 2018; 12:635-644. [PMID: 30081234 PMCID: PMC6082921 DOI: 10.1016/j.omtn.2018.07.004] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Revised: 07/03/2018] [Accepted: 07/03/2018] [Indexed: 12/28/2022]
Abstract
N6-methyladenosine (m6A) modification is the most abundant RNA methylation modification and involves various biological processes, such as RNA splicing and degradation. Recent studies have demonstrated the feasibility of identifying m6A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m6A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m6A sites. To predict m6A sites accurately within genomic sequences, we trained an ensemble of three support vector machine classifiers that explore the position-specific information and physical chemical information from position-specific k-mer nucleotide propensity, physical-chemical properties, and ring-function-hydrogen-chemical properties. We examined and compared the performance of our predictor with other state-of-the-art methods of benchmarking datasets. Comparative results showed that the proposed M6APred-EL performed more accurately for m6A site identification. Moreover, a user-friendly web server that implements the proposed M6APred-EL is well established and is currently available at http://server.malab.cn/M6APred-EL/. It is expected to be a practical and effective tool for the investigation of m6A functional mechanisms.
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Affiliation(s)
- Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin, China; State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China
| | - Huangrong Chen
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Ran Su
- School of Computer Software, Tianjin University, Tianjin, China; State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China.
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152
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Zhao Q, Zhang Y, Hu H, Ren G, Zhang W, Liu H. IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction. Front Genet 2018; 9:239. [PMID: 30023002 PMCID: PMC6040094 DOI: 10.3389/fgene.2018.00239] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 06/15/2018] [Indexed: 11/13/2022] Open
Abstract
Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.
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Affiliation(s)
- Qi Zhao
- School of Mathematics, Liaoning University, Shenyang, China.,Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China
| | - Yue Zhang
- School of Mathematics, Liaoning University, Shenyang, China
| | - Huan Hu
- School of Life Science, Liaoning University, Shenyang, China
| | - Guofei Ren
- School of Information, Liaoning University, Shenyang, China
| | - Wen Zhang
- School of Computer, Wuhan University, Wuhan, China
| | - Hongsheng Liu
- Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China.,School of Life Science, Liaoning University, Shenyang, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, China
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153
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Chen X, Qu J, Yin J. TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction. Front Genet 2018; 9:234. [PMID: 30018632 PMCID: PMC6038677 DOI: 10.3389/fgene.2018.00234] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 06/12/2018] [Indexed: 12/12/2022] Open
Abstract
In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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154
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Yu AZ, Ramsey SA. A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease. Interdiscip Sci 2018; 10:449-454. [PMID: 27778232 PMCID: PMC5403631 DOI: 10.1007/s12539-016-0194-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 10/10/2016] [Accepted: 10/13/2016] [Indexed: 10/20/2022]
Abstract
We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method, we integrate: (1) a dataset of transcriptome responses of a cell line to a panel of drugs; (2) two sets of genes for the disease; and (3) mappings between drugs and the receptors or pathways that they target. We carried out a gene set enrichment analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anti-correlation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (1) CMap 2.0; (2) gene sets from two transcriptome profiling studies of atherosclerosis; and (3) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPARγ; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, δ-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening.
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Affiliation(s)
- Alvin Z Yu
- Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, OR, 97331, USA
| | - Stephen A Ramsey
- Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, OR, 97331, USA.
- School of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR, 97331, USA.
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155
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Huang Y, Chang Z, Li X, Liang S, Yi Y, Wu L. Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder. Int J Biol Sci 2018; 14:811-818. [PMID: 29989084 PMCID: PMC6036758 DOI: 10.7150/ijbs.24624] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 03/14/2018] [Indexed: 12/26/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disease in early childhood, and growing up to be a major cause of disability in children. However, the underlying molecular mechanism of ASD remains elusive. Hence, we represented integrated multifactor analysis exploring dysfunctional modules based on RNA-Seq data from corpus callosum in 6 patients with ASD and 6 normal individuals. According to protein-protein interactions (PPIs) and WGCNA, we performed co-expression modules analysis for ASD-associated genes, and identified 25 modules with differentially expressed genes (DEGs), observing that genes in these modules were significantly involved in various biological processes in nervous system, sensory system, phylogenetic system and variety of signaling pathways. Then, based on transcriptional and post-transcriptional regulations, integrating transcription factor (TF)-target and RNA-associated interactions, significant regulators of co-expression modules were identified as pivot regulators, including 67 pivot TFs, 13 pivot miRNAs and 6 pivot lncRNAs. GO and KEGG pathway enrichment analysis demonstrated that the pivot miRNAs significantly enriched in neural or mental-associated biological progresses. The pivot TFs were mainly involved in various regulation of transcription, immune system and organs development. Finally, our work deciphered a multifactor dysfunctional co-expression subnetwork involved in ASD, helps uncover core dysfunctional modules for this disease and improves our understanding of its underlying molecular mechanism.
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Affiliation(s)
- Yan Huang
- Department of Child and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, China
| | - Zhenghong Chang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiaodan Li
- Department of Child and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, China
| | - Shuang Liang
- Department of Child and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, China
| | - Ying Yi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lijie Wu
- Department of Child and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, China
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156
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Yu L, Zhao J, Gao L. Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity. Int J Biol Sci 2018; 14:971-982. [PMID: 29989066 PMCID: PMC6036744 DOI: 10.7150/ijbs.23350] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 12/14/2017] [Indexed: 02/01/2023] Open
Abstract
Network-based computational method, with the emphasis on biomolecular interactions and biological data integration, has succeeded in drug development and created new directions, such as drug repositioning and drug combination. Drug repositioning, that is finding new uses for existing drugs to treat more patients, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Moreover, cumulative studies demonstrate that the mature miRNAs as well as their precursors can be targeted by small molecular drugs. At the same time, human diseases result from the disordered interplay of tissue- and cell lineage-specific processes. However, few computational researches predict drug-disease potential relationships based on miRNA data and tissue specificity. Therefore, based on miRNA data and the tissue specificity of diseases, we propose a new method named as miTS to predict the potential treatments for diseases. Firstly, based on miRNAs data, target genes and information of FDA (Food and Drug Administration) approved drugs, we evaluate the relationships between miRNAs and drugs in the tissue-specific PPI (protein-protein) network. Then, we construct a tripartite network: drug-miRNA-disease Finally, we obtain the potential drug-disease associations based on the tripartite network. In this paper, we take breast cancer as case study and focus on the top-30 predicted drugs. 25 of them (83.3%) are found having known connections with breast cancer in CTD (Comparative Toxicogenomics Database) benchmark and the other 5 drugs are potential drugs for breast cancer. We further evaluate the 5 newly predicted drugs from clinical records, literature mining, KEGG pathways enrichment analysis and overlapping genes between enriched pathways. For each of the 5 new drugs, strongly supported evidences can be found in three or more aspects. In particular, Regorafenib (DB08896) has 15 overlapping KEGG pathways with breast cancer and their p-values are all very small. In addition, whether in the literature curation or clinical validation, Regorafenib has a strong correlation with breast cancer. All the facts show that Regorafenib is likely to be a truly effective drug, worthy of our further study. It further follows that our method miTS is effective and practical for predicting new drug indications, which will provide potential values for treatments of complex diseases.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, P.R. China
| | - Jin Zhao
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, P.R. China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, P.R. China
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157
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Zhang E, Ma X. Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages. Molecules 2018; 23:molecules23051016. [PMID: 29701681 PMCID: PMC6102576 DOI: 10.3390/molecules23051016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 04/23/2018] [Accepted: 04/23/2018] [Indexed: 02/01/2023] Open
Abstract
Discovering the common modules that are co-expressed across various stages can lead to an improved understanding of the underlying molecular mechanisms of cancers. There is a shortage of efficient tools for integrative analysis of gene expression and protein interaction networks for discovering common modules associated with cancer progression. To address this issue, we propose a novel regularized multi-view subspace clustering (rMV-spc) algorithm to obtain a representation matrix for each stage and a joint representation matrix that balances the agreement across various stages. To avoid the heterogeneity of data, the protein interaction network is incorporated into the objective of rMV-spc via regularization. Based on the interior point algorithm, we solve the optimization problem to obtain the common modules. By using artificial networks, we demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy. Furthermore, the rMV-spc discovers common modules in breast cancer networks based on the breast data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm effectively integrate heterogeneous data for dynamic modules.
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Affiliation(s)
- Enli Zhang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
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158
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Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association. Sci Rep 2018; 8:6481. [PMID: 29691434 PMCID: PMC5915491 DOI: 10.1038/s41598-018-24532-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/03/2018] [Indexed: 12/15/2022] Open
Abstract
microRNAs (miRNAs) mutation and maladjustment are related to the occurrence and development of human diseases. Studies on disease-associated miRNA have contributed to disease diagnosis and treatment. To address the problems, such as low prediction accuracy and failure to predict the relationship between new miRNAs and diseases and so on, we design a Laplacian score of graphs to calculate the global similarity of networks and propose a Global Similarity method based on a Two-tier Random Walk for the prediction of miRNA-disease association (GSTRW) to reveal the correlation between miRNAs and diseases. This method is a global approach that can simultaneously predict the correlation between all diseases and miRNAs in the absence of negative samples. Experimental results reveal that this method is better than existing approaches in terms of overall prediction accuracy and ability to predict orphan diseases and novel miRNAs. A case study on GSTRW for breast cancer and conlon cancer is also conducted, and the majority of miRNA-disease association can be verified by our experiment. This study indicates that this method is feasible and effective.
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159
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Arun K, Arunkumar G, Bennet D, Chandramohan SM, Murugan AK, Munirajan AK. Comprehensive analysis of aberrantly expressed lncRNAs and construction of ceRNA network in gastric cancer. Oncotarget 2018; 9:18386-18399. [PMID: 29719612 PMCID: PMC5915079 DOI: 10.18632/oncotarget.24841] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 02/28/2018] [Indexed: 12/19/2022] Open
Abstract
Gastric cancer remains fifth most common cancer often diagnosed at an advanced stage and is the second leading cause of cancer-related death worldwide. Long non-coding RNAs (lncRNAs) involved in various cellular pathways are essential for tumor occurrence and progression and they have high potential to promote or suppress the expression of many genes. In this study, we profiled 19 selected cancer-associated lncRNAs in thirty gastric adenocarcinomas and matching normal tissues by qRT-PCR. Our results showed that most of the lncRNAs were significantly upregulated (12/19). Further, we performed bioinformatic screening of miRNAs that share common miRNA response elements (MREs) with lncRNAs and their downstream mRNA targets. The prediction identified three microRNAs (miR-21, miR-145 and miR-148a) and five gastric cancer-specific target genes (EGFR, KLF4, DNMT1 and AGO4) which also showed strong correlation with lncRNAs in regression analysis. Finally, we constructed an integrated lncRNA-miRNA-mRNA interaction network of the candidate genes to understand the post-transcriptional gene regulation. The ceRNA network analysis revealed that the differentially regulated miR-21 and miR-148a were playing as central candidates coordinating sponging activity of the lncRNAs analyzed (H19, TUG1 and MALAT1) in this study and the overexpression of H19 and miR-21 could be a signature event of gastric tumorigenesis that could serve as prognostic indicators and therapeutic targets.
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Affiliation(s)
- Kanagaraj Arun
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai - 600 113, India
| | - Ganesan Arunkumar
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai - 600 113, India
| | - Duraisamy Bennet
- Institute of Surgical Gastroenterology, Rajiv Gandhi Government General Hospital and Madras Medical College, Chennai - 600 001, India
| | - Servarayan Murugesan Chandramohan
- Institute of Surgical Gastroenterology, Rajiv Gandhi Government General Hospital and Madras Medical College, Chennai - 600 001, India
| | - Avaniyapuram Kannan Murugan
- Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre, Riyadh-11211, Saudi Arabia
| | - Arasambattu Kannan Munirajan
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai - 600 113, India
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160
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SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction. BIOMED RESEARCH INTERNATIONAL 2018; 2018:5747489. [PMID: 29750163 PMCID: PMC5884242 DOI: 10.1155/2018/5747489] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 01/23/2018] [Indexed: 12/12/2022]
Abstract
Aberrant expression of microRNAs (miRNAs) can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP). The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.
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161
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Zhao L, Ni X, Zhao L, Zhang Y, Jin D, Yin W, Wang D, Zhang W. MiroRNA-188 Acts as Tumor Suppressor in Non-Small-Cell Lung Cancer by Targeting MAP3K3. Mol Pharm 2018. [PMID: 29528232 DOI: 10.1021/acs.molpharmaceut.8b00071] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer. MicroRNAs have been increasingly implicated in NSCLC and may serve as novel therapeutic targets to combat cancer. Here we investigated the functional implication of miR-188 in NSCLC. We first analyzed miR-188 expression in both NSCLC clinical samples and cancer cell lines. Next we investigated its role in A549 and H2126 cells with cell proliferation, migration, and apoptosis assays. To extend the in vitro study, we employed both xenograft model and LSL- K-ras G12D lung cancer model to examine the role of miR-188 in tumorigenesis. Last we tested MAP3K3 as miR-188 target in NSCLC model. MiR-188 expression was significantly downregulated at the NSCLC tumor sites and lung cancer cells. In vitro transfection of miR-188 reduced cell proliferation and migration potential and promoted cell apoptosis. In xenograft model, miR-188 inhibited tumor growth derived from cancer cells. Intranasal miR-188 administration reduced tumor formation in NSCLC animal model. MAP3K3 was validated as direct target of miR-188. Knocking down MAP3K3 in mice also inhibited tumorigenesis in LSL- K-ras G12D model. Our results demonstrate that miR-188 and its downstream target MAP3K3 could be a potential therapeutic target for NSCLC.
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Affiliation(s)
- Lili Zhao
- Department of Respiratory Medicine , Affiliated Hongqi Hospital of Mudanjiang Medical University , Mudanjiang 157011 , Heilongjiang , P. R. China
| | - Xin Ni
- Department of Respiratory Medicine , Affiliated Hongqi Hospital of Mudanjiang Medical University , Mudanjiang 157011 , Heilongjiang , P. R. China
| | - Linlin Zhao
- Department of Respiratory Medicine , Affiliated Hongqi Hospital of Mudanjiang Medical University , Mudanjiang 157011 , Heilongjiang , P. R. China
| | - Yao Zhang
- Department of Respiratory Medicine , Affiliated Hongqi Hospital of Mudanjiang Medical University , Mudanjiang 157011 , Heilongjiang , P. R. China
| | - Dan Jin
- Department of Ultrasound , Mudanjiang Women and Children's Hospital , Mudanjiang 157000 , Heilongjiang , P. R. China
| | - Wei Yin
- Department of Bone Surgery , Mudanjiang Forestry Hospital , Mudanjiang 157000 , Heilongjiang , P. R. China
| | - Dandan Wang
- Department of Respiratory Medicine , Affiliated Hongqi Hospital of Mudanjiang Medical University , Mudanjiang 157011 , Heilongjiang , P. R. China
| | - Wei Zhang
- Department of Respiratory Medicine , Affiliated Hongqi Hospital of Mudanjiang Medical University , Mudanjiang 157011 , Heilongjiang , P. R. China
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162
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Liu Y, Luo J, Ding P. Inferring MicroRNA Targets Based on Restricted Boltzmann Machines. IEEE J Biomed Health Inform 2018; 23:427-436. [PMID: 29993787 DOI: 10.1109/jbhi.2018.2814609] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Predicting the miRNA-target interactions (MTIs) is a critical task for elucidating mechanistic roles of miRNAs in pathophysiology. However, most existing techniques have a higher false positive because the precise miRNA target mechanisms are poorly known. Considering that ensemble methods can take advantage of the complementary knowledge in different methods, we propose an alternative optimization framework, Inferring MiRNA Targets based on Restricted Boltzmann Machines (IMTRBM), to enhance the accuracy of previous prediction results. First, the proposed method directly constructs a weighted MTI network though the results predicted by individual methods and each miRNA target pair is weighted based on the frequency appearing in these results. Second, we transform the miRNA-target prediction problem into a complete bipartite graph model, named restricted Boltzmann machine, and utilize a practical learning procedure to train our model and make predictions. Our results show that the algorithm outperforms individual miRNA-target prediction approach in the number of validated miRNA targets at cutoffs of top list. Moreover, our framework can tolerate the decrease and increase of predicted MTIs and even discover new miRNA targets, which have been a challenge to predict for any individual methods. Finally, for the miRNAs that are not appearing in IMTRBM, we design a new method to supplement IMTRBM based on the intuition that similar miRNAs have similar functions, which also achieves a comparable result. The source code of IMTRBM is available at https://github.com/liuying201705/IMTRBM.
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163
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Wang X, Peng F, Cheng L, Yang G, Zhang D, Liu J, Chen X, Zhao S. Prognostic and clinicopathological role of long non-coding RNA UCA1 in various carcinomas. Oncotarget 2018; 8:28373-28384. [PMID: 28423704 PMCID: PMC5438656 DOI: 10.18632/oncotarget.16059] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 02/27/2017] [Indexed: 12/26/2022] Open
Abstract
Urothelial cancer associated 1 (UCA1) as an oncogenic long non-coding RNA (LncRNA) was aberrantly upregulated in various solid tumors. Numerous studies have demonstrated overexpression of UCA1 is an unfavorable prognostic indicator in cancer patients. This study aimed to further explore the prognosis role and clinical significance of UCA1 in cancer. Eligible studies were recruited by a systematic search in PubMed, Embase, Cochrane Library and Web of Science databases. A total of 19/16 studies with 1587/1291 cancer patients were included to evaluate the association between UCA1 expression and overall survival (OS) and clinicopathological factors of malignancies by computing hazard ratio (HR), odds ratios (OR) and confidence interval (CI). The meta-analysis indicated overexpression of UCA1 was significantly correlated with unexpected OS in patients with cancer (pooled HR = 1.85, 95% CI 1.62-2.10, p < 0.001). There was also a significantly negative association between high level of UCA1 and poor grade cancer (pooled OR = 2.74, 95% CI 2.04-3.70, p < 0.001) and positive lymphatic metastasis (pooled OR = 2.43, 95% CI 1.72-3.41, p < 0.001). In conclusion, our study suggested that UCA1 was correlated with more advanced clinicopathological features and poor prognosis as a novel predictive biomarker of patients with various tumors.
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Affiliation(s)
- Xiaoxiong Wang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
| | - Fei Peng
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150081, People's Republic of China
| | - Guang Yang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
| | - Daming Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
| | - Jiaqi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
| | - Xin Chen
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
| | - Shiguang Zhao
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China.,Institute of Brain Science, Harbin Medical University, Nangang District, Harbin, Heilongjiang Province, 150001, People's Republic of China
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164
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Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis. Oncotarget 2018; 7:85613-85623. [PMID: 27784000 PMCID: PMC5356763 DOI: 10.18632/oncotarget.12828] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Accepted: 10/14/2016] [Indexed: 01/09/2023] Open
Abstract
MicroRNAs(miRNAs) often exert their oncogenic and tumor suppressor functions by suppressing protein-coding genes expressions in cancers and thus have a strong association with cancers' generation, development and metastasis. Through comprehensively understanding differentially expressed miRNAs (oncomiRNA) in tumor tissues, we can elucidate the underlying molecular mechanisms in tumorigenesis and develop novel strategies for cancer diagnosis and treatment. The differential expression of miRNAs can now be analyzed through numerous statistical significance tests based on different principles, which are also available in various R packages. However, the results can be notably different. In this study, we compared miRNAs obtained from 6 common significance tests/R packages (t-test, Limma, DESeq, edgeR, LRT and MARS) with the miRNAs archived in two databases; HMDD 2.0 database, which collects experimentally validated differentially expressed miRNAs, and Infer microRNA-disease association database, which contains the potential disease-associated miRNAs by network forecasting. Finally, we sought the MARS method in DEGseq package more effectively searched out differentially expressed miRNAs than other common methods.
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165
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Wang X, Liao Z, Bai Z, He Y, Duan J, Wei L. MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification. Genes (Basel) 2018; 9:genes9010051. [PMID: 29361788 PMCID: PMC5793202 DOI: 10.3390/genes9010051] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 01/15/2018] [Accepted: 01/16/2018] [Indexed: 12/11/2022] Open
Abstract
Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A, formerly known as PGC-1a) is a transcriptional coactivator and metabolic regulator. Previous studies are mainly focused on the association between PPARGC1A and hepatoma. However, the regulatory mechanism remains unknown. A microRNA associated with cancer (oncomiR), miR-93-5p, has recently been found to play an essential role in tumorigenesis and progression of various carcinomas, including liver cancer. Therefore, this paper aims to explore the regulatory mechanism underlying these two proteins in hepatoma cells. Firstly, an integrative analysis was performed with miRNA–mRNA modules on microarray and The Cancer Genome Atlas (TCGA) data and obtained the core regulatory network and miR-93-5p/PPARGC1A pair. Then, a series of experiments were conducted in hepatoma cells with the results including miR-93-5p upregulated and promoted cell proliferation. Thirdly, the inverse correlation between miR-93-5p and PPARGC1A expression was validated. Finally, we inferred that miR-93-5p plays an essential role in inhibiting PPARGC1A expression by directly targeting the 3′-untranslated region (UTR) of its mRNA. In conclusion, these results suggested that miR-93-5p overexpression contributes to hepatoma development by inhibiting PPARGC1A. It is anticipated to be a promising therapeutic strategy for patients with liver cancer in the future.
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Affiliation(s)
- Xinrui Wang
- State Key Laboratory for Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China.
| | - Zhijun Liao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China.
| | - Zhimin Bai
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China.
- Department of Clinical Laboratory, Jinjiang Municipal Hospital, Jinjiang 362200, China.
| | - Yan He
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China.
| | - Juan Duan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China.
| | - Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
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166
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167
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Jiang J, Xing F, Wang C, Zeng X. Identification and Analysis of Rice Yield-Related Candidate Genes by Walking on the Functional Network. FRONTIERS IN PLANT SCIENCE 2018; 9:1685. [PMID: 30524460 PMCID: PMC6262309 DOI: 10.3389/fpls.2018.01685] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/30/2018] [Indexed: 05/04/2023]
Abstract
Rice (Oryza sativa L.) is one of the most important staple foods in the world. It is possible to identify candidate genes associated with rice yield using the model of random walk with restart on a functional similarity network. We demonstrated the high performance of this approach by a five-fold cross-validation experiment, as well as the robustness of the parameter r. We also assessed the strength of associations between known seeds and candidate genes in the light of the results scores. The candidates ranking at the top of the results list were considered to be the most relevant rice yield-related genes. This study provides a valuable alternative for rice breeding and biology research. The relevant dataset and script can be downloaded at the website: http://lab.malab.cn/jj/rice.htm.
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Affiliation(s)
- Jing Jiang
- School of Aerospace Engineering, Xiamen University, Xiamen, China
| | - Fei Xing
- School of Aerospace Engineering, Xiamen University, Xiamen, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
- *Correspondence: Chunyu Wang, Xiangxiang Zeng,
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Xiamen University, Xiamen, China
- *Correspondence: Chunyu Wang, Xiangxiang Zeng,
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168
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Zhang Y, Li W, Feng Y, Guo S, Zhao X, Wang Y, He Y, He W, Chen L. Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks. Oncotarget 2017; 8:103375-103384. [PMID: 29262568 PMCID: PMC5732734 DOI: 10.18632/oncotarget.21874] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 10/04/2017] [Indexed: 12/16/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.
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Affiliation(s)
- Yihua Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yuyan Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Shanshan Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xilei Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, Heilongjiang Province, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
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169
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Chen H, Zhang Z, Peng W. miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships. Sci Rep 2017; 7:15921. [PMID: 29162848 PMCID: PMC5698443 DOI: 10.1038/s41598-017-15716-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 10/31/2017] [Indexed: 01/10/2023] Open
Abstract
Revealing the cause-and-effect mechanism behind drug-disease relationships remains a challenging task. Recent studies suggested that drugs can target microRNAs (miRNAs) and alter their expression levels. In the meanwhile, the inappropriate expression of miRNAs will lead to various diseases. Therefore, targeting specific miRNAs by small-molecule drugs to modulate their activities provides a promising approach to human disease treatment. However, few studies attempt to discover drug-disease causal relationships through the molecular level of miRNAs. Here, we developed a miRNA-based inference method miRDDCR to comprehensively predict drug-disease causal relationships. We first constructed a three-layer drug-miRNA-disease heterogeneous network by combining similarity measurements, existing drug-miRNA associations and miRNA-disease associations. Then, we extended the algorithm of Random Walk to the three-layer heterogeneous network and ranked the potential indications for drugs. Leave-one-out cross-validations and case studies demonstrated that our method miRDDCR can achieve excellent prediction power. Compared with related methods, our causality discovery-based algorithm showed superior prediction ability and highlighted the molecular basis miRNAs, which can be used to assist in the experimental design for drug development and disease treatment. Finally, comprehensively inferred drug-disease causal relationships were released for further studies.
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Affiliation(s)
- Hailin Chen
- School of Software, East China Jiaotong University, Nanchang, China.
| | - Zuping Zhang
- School of Information Science and Engineering, Central South University, Changsha, China
| | - Wei Peng
- Computer Center of Kunming University of Science and Technology, Kunming, China
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170
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RNA-sequencing reveals genome-wide long non-coding RNAs profiling associated with early development of diabetic nephropathy. Oncotarget 2017; 8:105832-105847. [PMID: 29285296 PMCID: PMC5739683 DOI: 10.18632/oncotarget.22405] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 09/08/2017] [Indexed: 02/05/2023] Open
Abstract
Background Diabetic nephropathy (DN) seriously threatens the lives of patients, and the mechanism of DN remains largely unknown because of the complex regulation between long non-coding RNA (lncRNA) and protein-coding genes. In early development of diabetic nephropathy (DN), pathogenesis remains largely unknown. Results We used RNA-sequencing to profile protein-coding and lncRNA gene transcriptome of mouse kidney proximal tubular cells during early stage of DN at various time points. Over 7000 protein-coding and lncRNA genes were differentially expressed, and most of them were time-specific. Nearly 40% of lncRNA genes overlapped with functional element signals using CHIP-Seq data from ENCODE database. Disease progression was characterized by lncRNA expression patterns, rather than protein-coding genes, indicating that the lncRNA genes are potential biomarkers for DN. For gene ontologies related to kidney, enrichment was observed in protein-coding genes co-expressed with neighboring lncRNA genes. Based on protein-coding and lncRNA gene profiles, clustering analysis reveals dynamic expression patterns for kidney, suggesting that they are highly correlated during disease progression. To evaluate translation of mouse model to human conditions, we experimentally validated orthologous genes in human cells in vitro diabetic model. In mouse model, most gene expression patterns were repeated in human cell lines. Conclusions These results define dynamic transcriptome and novel functional roles for lncRNAs in diabetic kidney cells; these roles may result in lncRNA-based diagnosis and therapies for DN.
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171
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Wang X, Sun B, Liu B, Fu Y, Zheng P. A novel method for multifactorial bio-chemical experiments design based on combinational design theory. PLoS One 2017; 12:e0186853. [PMID: 29095845 PMCID: PMC5667848 DOI: 10.1371/journal.pone.0186853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 10/09/2017] [Indexed: 11/19/2022] Open
Abstract
Experimental design focuses on describing or explaining the multifactorial interactions that are hypothesized to reflect the variation. The design introduces conditions that may directly affect the variation, where particular conditions are purposely selected for observation. Combinatorial design theory deals with the existence, construction and properties of systems of finite sets whose arrangements satisfy generalized concepts of balance and/or symmetry. In this work, borrowing the concept of "balance" in combinatorial design theory, a novel method for multifactorial bio-chemical experiments design is proposed, where balanced templates in combinational design are used to select the conditions for observation. Balanced experimental data that covers all the influencing factors of experiments can be obtianed for further processing, such as training set for machine learning models. Finally, a software based on the proposed method is developed for designing experiments with covering influencing factors a certain number of times.
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Affiliation(s)
- Xun Wang
- College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China
| | - Beibei Sun
- College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China
| | - Boyang Liu
- State-owned Asset and Laboratory Management Department, China University of Petroleum, Qingdao 266580, Shandong, China
| | - Yaping Fu
- Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong, China
- * E-mail: (YF); (PZ)
| | - Pan Zheng
- Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching 93350, Malaysia
- * E-mail: (YF); (PZ)
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172
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Fu L, Peng Q. A deep ensemble model to predict miRNA-disease association. Sci Rep 2017; 7:14482. [PMID: 29101378 PMCID: PMC5670180 DOI: 10.1038/s41598-017-15235-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 10/23/2017] [Indexed: 02/08/2023] Open
Abstract
Cumulative evidence from biological experiments has confirmed that microRNAs (miRNAs) are related to many types of human diseases through different biological processes. It is anticipated that precise miRNA-disease association prediction could not only help infer potential disease-related miRNA but also boost human diagnosis and disease prevention. Considering the limitations of previous computational models, a more effective computational model needs to be implemented to predict miRNA-disease associations. In this work, we first constructed a human miRNA-miRNA similarity network utilizing miRNA-miRNA functional similarity data and heterogeneous miRNA Gaussian interaction profile kernel similarities based on the assumption that similar miRNAs with similar functions tend to be associated with similar diseases, and vice versa. Then, we constructed disease-disease similarity using disease semantic information and heterogeneous disease-related interaction data. We proposed a deep ensemble model called DeepMDA that extracts high-level features from similarity information using stacked autoencoders and then predicts miRNA-disease associations by adopting a 3-layer neural network. In addition to five-fold cross-validation, we also proposed another cross-validation method to evaluate the performance of the model. The results show that the proposed model is superior to previous methods with high robustness.
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Affiliation(s)
- Laiyi Fu
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China
| | - Qinke Peng
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China.
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173
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Jiang X, Zhang H, Duan F, Quan X. Identify Huntington's disease associated genes based on restricted Boltzmann machine with RNA-seq data. BMC Bioinformatics 2017; 18:447. [PMID: 29020921 PMCID: PMC5637347 DOI: 10.1186/s12859-017-1859-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 10/02/2017] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression. RESULTS In this paper, we propose a deep learning approach based on the restricted Boltzmann machine to analyze the RNA-seq data of Huntington's disease, namely stacked restricted Boltzmann machine (SRBM). According to the SRBM, we also design a novel framework to screen the key genes during the Huntington's disease development. In this work, we assume that the effects of regulatory factors can be captured by the hierarchical structure and narrow hidden layers of the SRBM. First, we select disease-associated factors with different time period datasets according to the differentially activated neurons in hidden layers. Then, we select disease-associated genes according to the changes of the gene energy in SRBM at different time periods. CONCLUSIONS The experimental results demonstrate that SRBM can detect the important information for differential analysis of time series gene expression datasets. The identification accuracy of the disease-associated genes is improved to some extent using the novel framework. Moreover, the prediction precision of disease-associated genes for top ranking genes using SRBM is effectively improved compared with that of the state of the art methods.
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Affiliation(s)
- Xue Jiang
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China
| | - Han Zhang
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China
| | - Feng Duan
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China
| | - Xiongwen Quan
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China. .,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China.
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174
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Proctor CJ, Goljanek-Whysall K. Using computer simulation models to investigate the most promising microRNAs to improve muscle regeneration during ageing. Sci Rep 2017; 7:12314. [PMID: 28951568 PMCID: PMC5614911 DOI: 10.1038/s41598-017-12538-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 09/05/2017] [Indexed: 01/17/2023] Open
Abstract
MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis.
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Affiliation(s)
- Carole J Proctor
- MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Institute of Cellular Medicine and Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK.
| | - Katarzyna Goljanek-Whysall
- MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
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175
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Wang X, Li W, Zhang Y, Feng Y, Zhao X, He Y, Zhang J, Chen L. Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information. PLoS One 2017; 12:e0184299. [PMID: 28873096 PMCID: PMC5584748 DOI: 10.1371/journal.pone.0184299] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/21/2017] [Indexed: 02/07/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.
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Affiliation(s)
- Xinyan Wang
- Department of Respiratory, the Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yihua Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yuyan Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xilei Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Jun Zhang
- Department of pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, Heilongjiang, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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176
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Pallez D, Gardès J, Pasquier C. Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis. Sci Rep 2017; 7:10548. [PMID: 28874691 PMCID: PMC5585369 DOI: 10.1038/s41598-017-10065-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 07/26/2017] [Indexed: 12/29/2022] Open
Abstract
MicroRNAs, small non-coding elements implied in gene regulation, are very interesting biomarkers for various diseases such as cancers. They represent potential prodigious biotechnologies for early diagnosis and gene therapies. However, experimental verification of microRNA-disease associations are time-consuming and costly, so that computational modeling is a proper solution. Previously, we designed MiRAI, a predictive method based on distributional semantics, to identify new associations between microRNA molecules and human diseases. Our preliminary results showed very good prediction scores compared to other available methods. However, MiRAI performances depend on numerous parameters that cannot be tuned manually. In this study, a parallel evolutionary algorithm is proposed for finding an optimal configuration of our predictive method. The automatically parametrized version of MiRAI achieved excellent performance. It highlighted new miRNA-disease associations, especially the potential implication of mir-188 and mir-795 in various diseases. In addition, our method allowed to detect several putative false associations contained in the reference database.
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Affiliation(s)
- Denis Pallez
- Université Côte d'Azur, CNRS, I3S, Sophia Antipolis, France
| | - Julien Gardès
- BIOMANDA, 2720 Chemin St Bernard, Les Moulins I Batiment 4, 06220, Vallauris, France
| | - Claude Pasquier
- Université Côte d'Azur, CNRS, I3S, Sophia Antipolis, France.
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177
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Zhong Y, Xuan P, Wang X, Zhang T, Li J, Liu Y, Zhang W. A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network. Bioinformatics 2017; 34:267-277. [PMID: 28968753 DOI: 10.1093/bioinformatics/btx546] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 07/23/2017] [Accepted: 08/31/2017] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. RESULTS We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. AVAILABILITY AND IMPLEMENTATION The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yingli Zhong
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Xiao Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin, China
| | - Jianzhong Li
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Yong Liu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Weixiong Zhang
- College of Math and Computer Science, Institute for Systems Biology, Jianghan University, Wuhan, China.,Department of Computer Science and Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA
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178
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Li L, Wang Y, An L, Kong X, Huang T. A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease. PLoS One 2017; 12:e0182592. [PMID: 28787010 PMCID: PMC5546581 DOI: 10.1371/journal.pone.0182592] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 07/20/2017] [Indexed: 12/28/2022] Open
Abstract
As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.
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Affiliation(s)
- Lin Li
- Department of Otorhinolaryngology and Head & Neck, China-Japan Union Hospital of Jilin University, Changchun, China
| | - YanShu Wang
- Department of Anesthesia, The First Hospital of Jilin University, Changchun, China
| | - Lifeng An
- Department of Otorhinolaryngology and Head & Neck, China-Japan Union Hospital of Jilin University, Changchun, China
- * E-mail:
| | - XiangYin Kong
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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179
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Wei Y, Yan Z, Wu C, Zhang Q, Zhu Y, Li K, Xu Y. Integrated analysis of dosage effect lncRNAs in lung adenocarcinoma based on comprehensive network. Oncotarget 2017; 8:71430-71446. [PMID: 29069717 PMCID: PMC5641060 DOI: 10.18632/oncotarget.19864] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 07/25/2017] [Indexed: 02/07/2023] Open
Abstract
Accumulating evidences indicate that cancer-related lncRNAs occur frequent somatic copy number alternation (SCNA). Although individual SCNA lncRNAs have been implicated in tumor biology, their regulatory mechanism has not been assessed in a systematic way. In order to explore the expression characteristics and biological functions of SCNA lncRNAs in cancer, we built a computational framework based on lncRNA expression profiles, lncRNA copy numbers and dosage sensitivity score (DSS). First, we found that the lncRNAs with different DSS were involved in distinct biological processes, while those with the same DSS had similar functions. Second, some of the lncRNAs participated in the progression and metastasis of lung adenocarcinoma (LUAD) through cis-acting regulation. In lncRNA-TF-mRNA network, lncRNAs interacted with 4 TFs and affected the immune system, and further influenced LUAD progression. Third, competing endogenous RNA network analysis inferred that lncRNA ENSG00000240990 competed with HOXA10 to absorb hsa-let-7a/b/f/g-5p and affected patient prognosis in LUAD. Last but not least, by integrating target information of miRNA we also provided a new perspective for the discovery of potential small molecule drugs. In summary, we systematically analyzed the regulatory role of SCNA lncRNAs. This work may facilitate cancer research and serve as the basis for future efforts to understand the role of SCNA lncRNAs, develop novel biomarkers and improve knowledge of tumor biology.
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Affiliation(s)
- Yunzhen Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zichuang Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Cheng Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qiang Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yinling Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kun Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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180
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Han Y, Sun W, Sun G, Hou X, Gong Z, Xu J, Bai X, Fu L. A 3-year observation of testosterone deficiency in Chinese patients with chronic heart failure. Oncotarget 2017; 8:79835-79842. [PMID: 29108365 PMCID: PMC5668098 DOI: 10.18632/oncotarget.19816] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 07/12/2017] [Indexed: 12/11/2022] Open
Abstract
Testosterone deficiency is present in a certain proportion men with chronic heart failure (CHF). Low testosterone levels in American and European patients with CHF lead to the high mortality and readmission rates. Interestingly, this relationship has not been studied in Chinese patients. To this end, 167 Chinese men with CHF underwent clinical and laboratory evaluations associated with determinations of testosterone levels. Total testosterone (TT) levels and sex hormone-binding globulin were measured by chemiluminescence or immunoassays assays and free testosterone (FT) levels were calculated, Based upon results from these assays, patients were divided into either a low testosterone (LT; n = 93) or normal testosterone (NT; n = 74) group. Subsequently, records from each patient were reviewed over a follow-up duration of at least 3 years. Patients in the LT group experienced worse cardiac function and a higher prevalence of etiology (ischemic vs. no ischemic) and comorbidity (both P < 0.05). In addition, readmission rates of patients in the LT group were higher than that of patients in the NT group (3.32 ± 1.66 VS 1.57 ± 0.89). Overall, deficiencies in FT levels were accompanied with increased mortalities (HR = 6.301, 95% CI 3.187–12.459, P < .0001).
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Affiliation(s)
- Ying Han
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Weiju Sun
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Guizhi Sun
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Xiaolu Hou
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Zhaowei Gong
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Jing Xu
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Xiuping Bai
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Lu Fu
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
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181
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Zhang C, Wang X, Li X, Zhao N, Wang Y, Han X, Ci C, Zhang J, Li M, Zhang Y. The landscape of DNA methylation-mediated regulation of long non-coding RNAs in breast cancer. Oncotarget 2017; 8:51134-51150. [PMID: 28881636 PMCID: PMC5584237 DOI: 10.18632/oncotarget.17705] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 04/24/2017] [Indexed: 12/22/2022] Open
Abstract
Although systematic studies have identified a host of long non-coding RNAs (lncRNAs) which are involved in breast cancer, the knowledge about the methyla-tion-mediated dysregulation of those lncRNAs remains limited. Here, we integrated multi-omics data to analyze the methylated alteration of lncRNAs in breast invasive carcinoma (BRCA). We found that lncRNAs showed diverse methylation patterns on promoter regions in BRCA. LncRNAs were divided into two categories and four subcategories based on their promoter methylation patterns and expression levels be-tween tumor and normal samples. Through cis-regulatory analysis and gene ontology network, abnormally methylated lncRNAs were identified to be associated with can-cer regulation, proliferation or expression of transcription factors. Competing endog-enous RNA network and functional enrichment analysis of abnormally methylated lncRNAs showed that lncRNAs with different methylation patterns were involved in several hallmarks and KEGG pathways of cancers significantly. Finally, survival analysis based on mRNA modules in networks revealed that lncRNAs silenced by high methylation were associated with prognosis significantly in BRCA. This study enhances the understanding of aberrantly methylated patterns of lncRNAs and pro-vides a novel insight for identifying cancer biomarkers and potential therapeutic tar-gets in breast cancer.
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Affiliation(s)
- Chunlong Zhang
- Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163000, China
| | - Xinyu Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xuecang Li
- Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163000, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150081, China
| | - Yihan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xiaole Han
- Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163000, China
| | - Ce Ci
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jian Zhang
- Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163000, China
| | - Meng Li
- Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163000, China
| | - Yan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
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182
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Yu G, Fu G, Lu C, Ren Y, Wang J. BRWLDA: bi-random walks for predicting lncRNA-disease associations. Oncotarget 2017; 8:60429-60446. [PMID: 28947982 PMCID: PMC5601150 DOI: 10.18632/oncotarget.19588] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 06/19/2017] [Indexed: 12/20/2022] Open
Abstract
Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs Bi-Random Walks to predict novel LncRNA-Disease Associations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations.
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Affiliation(s)
- Guoxian Yu
- College of Computer and Information Sciences, Southwest University, Chongqing, China
| | - Guangyuan Fu
- College of Computer and Information Sciences, Southwest University, Chongqing, China
| | - Chang Lu
- College of Computer and Information Sciences, Southwest University, Chongqing, China
| | - Yazhou Ren
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jun Wang
- College of Computer and Information Sciences, Southwest University, Chongqing, China
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183
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Peng L, Peng M, Liao B, Huang G, Liang W, Li K. Improved low-rank matrix recovery method for predicting miRNA-disease association. Sci Rep 2017; 7:6007. [PMID: 28729528 PMCID: PMC5519594 DOI: 10.1038/s41598-017-06201-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 06/23/2017] [Indexed: 02/06/2023] Open
Abstract
MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research. Considering limitation of existing computational approaches, we develop improved low-rank matrix recovery (ILRMR) for miRNA-disease association prediction. ILRMR is a global method that can simultaneously prioritize potential association for all diseases and does not require negative samples. ILRMR can also identify promising miRNAs for investigating diseases without any known related miRNA. By integrating miRNA-miRNA similarity information, disease-disease similarity information, and miRNA family information to matrix recovery, ILRMR performs better than other methods in cross validation and case studies.
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Affiliation(s)
- Li Peng
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China.,College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China
| | - Manman Peng
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China.
| | - Bo Liao
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Guohua Huang
- College of Information Engineering, Shaoyang University, Shaoyang, Hunan, 422000, China
| | - Wei Liang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China
| | - Keqin Li
- Department of Computer Science, State University of New York, New Paltz, New York, 12561, USA
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184
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Jiang X, Zhang H, Quan X, Liu Z, Yin Y. Disease-related gene module detection based on a multi-label propagation clustering algorithm. PLoS One 2017; 12:e0178006. [PMID: 28542379 PMCID: PMC5438150 DOI: 10.1371/journal.pone.0178006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 05/06/2017] [Indexed: 01/11/2023] Open
Abstract
Detecting disease-related gene modules by analyzing gene expression data is of great significance. It is helpful for exploratory analysis of the interaction mechanisms of genes under complex disease phenotypes. The multi-label propagation algorithm (MLPA) has been widely used in module detection for its fast and easy implementation. The accuracy of MLPA greatly depends on the connections between nodes, and most existing research focuses on measuring the similarity between nodes. However, MLPA does not perform well with loose connections between disease-related genes. Moreover, the biological significance of modules obtained by MLPA has not been demonstrated. To solve these problems, we designed a double label propagation clustering algorithm (DLPCA) based on MLPA to study Huntington's disease. In DLPCA, in addition to category labels, we introduced pathogenic labels to supervise the process of multi-label propagation clustering. The pathogenic labels contain pathogenic information about disease genes and the hierarchical structure of gene expression data. Experimental results demonstrated the superior performance of DLPCA compared with other conventional gene-clustering algorithms.
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Affiliation(s)
- Xue Jiang
- College of Computer and Control Engineering, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Han Zhang
- College of Computer and Control Engineering, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Xiongwen Quan
- College of Computer and Control Engineering, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Zhandong Liu
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States of America
| | - Yanbin Yin
- Department of Biological Sciences, Northern Illinois University, DeKalb, IL 60115, United States of America
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185
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Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm. PLoS One 2017; 12:e0177017. [PMID: 28472169 PMCID: PMC5417634 DOI: 10.1371/journal.pone.0177017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 04/20/2017] [Indexed: 01/03/2023] Open
Abstract
Fruit is essential for plant reproduction and is responsible for protection and dispersal of seeds. The development and maturation of fruit is tightly regulated by numerous genetic factors that respond to environmental and internal stimulation. In this study, we attempted to identify novel fruit-related genes in a model organism, Arabidopsis thaliana, using a computational method. Based on validated fruit-related genes, the random walk with restart (RWR) algorithm was applied on a protein-protein interaction (PPI) network using these genes as seeds. The identified genes with high probabilities were filtered by the permutation test and linkage tests. In the permutation test, the genes that were selected due to the structure of the PPI network were discarded. In the linkage tests, the importance of each candidate gene was measured from two aspects: (1) its functional associations with validated genes and (2) its similarity with validated genes on gene ontology (GO) terms and KEGG pathways. Finally, 255 inferred genes were obtained, subsequent extensive analysis of important genes revealed that they mainly contribute to ubiquitination (UBQ9, UBQ8, UBQ11, UBQ10), serine hydroxymethyl transfer (SHM7, SHM5, SHM6) or glycol-metabolism (HXKL2_ARATH, CSY5, GAPCP1), suggesting essential roles during the development and maturation of fruit in Arabidopsis thaliana.
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186
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Guala D, Sonnhammer ELL. A large-scale benchmark of gene prioritization methods. Sci Rep 2017; 7:46598. [PMID: 28429739 PMCID: PMC5399445 DOI: 10.1038/srep46598] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 03/22/2017] [Indexed: 11/16/2022] Open
Abstract
In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology(GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.
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Affiliation(s)
- Dimitri Guala
- Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Erik L L Sonnhammer
- Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
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187
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Mugunga I, Ju Y, Liu X, Huang X. Computational prediction of human disease-related microRNAs by path-based random walk. Oncotarget 2017; 8:58526-58535. [PMID: 28938576 PMCID: PMC5601672 DOI: 10.18632/oncotarget.17226] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 03/22/2017] [Indexed: 01/09/2023] Open
Abstract
MicroRNAs (miRNAs) are a class of small, endogenous RNAs that are 21–25 nucleotides in length. In animals and plants, miRNAs target specific genes for degradation or translation repression. Discovering disease-related miRNA is fundamental for understanding the pathogenesis of diseases. The association between miRNA and a disease is mainly determined via biological investigation, which is complicated by increased biological information due to big data from different databases. Researchers have utilized different computational methods to harmonize experimental approaches to discover miRNA that articulates restrictively in specific environmental situations. In this work, we present a prediction model that is based on the theory of path features and random walk to obtain a relevancy score of miRNA-related disease. In this model, highly ranked scores are potential miRNA-disease associations. Features were extracted from positive and negative samples of miRNA-disease association. Then, we compared our method with other presented models using the five-fold cross-validation method, which obtained an area under the receiver operating characteristic curve of 88.6%. This indicated that our method has a better performance compared to previous methods and will help future biological investigations.
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Affiliation(s)
- Israel Mugunga
- Department of Computer Science, Xiamen University, Xiamen, 361005, China
| | - Ying Ju
- Department of Computer Science, Xiamen University, Xiamen, 361005, China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen, 361005, China
| | - Xiaoyang Huang
- Department of Computer Science, Xiamen University, Xiamen, 361005, China
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188
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Sun Z, Hao T, Tian J. Identification of exosomes and its signature miRNAs of male and female Cynoglossus semilaevis. Sci Rep 2017; 7:860. [PMID: 28408738 PMCID: PMC5429842 DOI: 10.1038/s41598-017-00884-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 03/16/2017] [Indexed: 12/20/2022] Open
Abstract
Exosomes are small membrane particles which are widely found in various cell lines and physiological fluids in mammalian. MicroRNAs (miRNAs) enclosed in exosomes have been identified as proper signatures for many diseases and response to therapies. However, the composition of exosomes and enclosed miRNAs in fishes has not been investigated. Cynoglossus semilaevis is an important commercial flatfish with ambiguous distinction between males and females before sex maturation, which leads to screening difficulty in reproduction and cultivation. An effective detection method was required for sex differentiation of C. semilaevis. In this work, we successfully identified exosomes in C. semilaevis serum. The analysis of nucleotide composition showed that miRNA dominated in exosomes. Thereafter the miRNA profiles in exosomes from males and females were sequenced and compared to identify the signature miRNAs corresponding to sex differentiation. The functions of signature miRNAs were analyzed by target matching and annotation. Furthermore, 7 miRNAs with high expression in males were selected from signature miRNAs as the markers for sex identification with their expression profiles verified by real time quantitative PCR. Exosomes were first found in fish serum in this work. Investigation of marker miRNAs supplies an effective index for the filtration of male and female C. semilaevis in cultivation.
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Affiliation(s)
- Zhanpeng Sun
- College of Life Sciences, Zhejiang University, Zhejiang, 310058, P.R. China
| | - Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin, 300387, P.R. China.
| | - Jinze Tian
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin, 300387, P.R. China
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189
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Peng H, Lan C, Zheng Y, Hutvagner G, Tao D, Li J. Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite. BMC Bioinformatics 2017; 18:193. [PMID: 28340554 PMCID: PMC5366146 DOI: 10.1186/s12859-017-1605-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 03/15/2017] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. METHODS AND RESULTS We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. CONCLUSIONS With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.
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Affiliation(s)
- Hui Peng
- Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia
| | - Chaowang Lan
- Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia
| | - Yi Zheng
- Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia
| | - Gyorgy Hutvagner
- Centre for Health Technologies, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia
| | - Dacheng Tao
- School of Information Technologies and the Faculty of Engineering and Information Technologies, University of Sydney, J12/318 Cleveland St, Darlington, 2008, NSW, Australia
| | - Jinyan Li
- Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia.
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190
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Yu H, Chen X, Lu L. Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm. Sci Rep 2017; 7:43792. [PMID: 28317855 PMCID: PMC5357838 DOI: 10.1038/srep43792] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/30/2017] [Indexed: 12/12/2022] Open
Abstract
Identification of the associations between microRNA molecules and human diseases from large-scale heterogeneous biological data is an important step for understanding the pathogenesis of diseases in microRNA level. However, experimental verification of microRNA-disease associations is expensive and time-consuming. To overcome the drawbacks of conventional experimental methods, we presented a combinatorial prioritization algorithm to predict the microRNA-disease associations. Importantly, our method can be used to predict microRNAs (diseases) associated with the diseases (microRNAs) without the known associated microRNAs (diseases). The predictive performance of our proposed approach was evaluated and verified by the internal cross-validations and external independent validations based on standard association datasets. The results demonstrate that our proposed method achieves the impressive performance for predicting the microRNA-disease association with the Area Under receiver operation characteristic Curve (AUC), 86.93%, which is indeed outperform the previous prediction methods. Particularly, we observed that the ensemble-based method by integrating the predictions of multiple algorithms can give more reliable and robust prediction than the single algorithm, with the AUC score improved to 92.26%. We applied our combinatorial prioritization algorithm to lung neoplasms and breast neoplasms, and revealed their top 30 microRNA candidates, which are in consistent with the published literatures and databases.
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Affiliation(s)
- Hua Yu
- State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, No. 1 West Beichen Road, Chaoyang District, Beijing, 100101, China
| | - Xiaojun Chen
- Key Lab of Agricultural Biotechnology of Ningxia, Agricultural Biotechnology Center, Ningxia Academy of Agriculture and Forestry Sciences, 590 Huanghe East Road, Jinfeng District, Yinchuan, Ningxia, 750002, China.
| | - Lu Lu
- Beijing Computing Center, Beijing Academy of Science and Technology, Building 3 BeiKe Industrial park, Fengxian road 7, Haidian District, Beijing, 100094, China
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191
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Lamurias A, Clarke LA, Couto FM. Extracting microRNA-gene relations from biomedical literature using distant supervision. PLoS One 2017; 12:e0171929. [PMID: 28263989 PMCID: PMC5338769 DOI: 10.1371/journal.pone.0171929] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 01/29/2017] [Indexed: 11/18/2022] Open
Abstract
Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.
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Affiliation(s)
- Andre Lamurias
- LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Luka A. Clarke
- BioISI: Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
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192
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Li CQ, Huang GW, Wu ZY, Xu YJ, Li XC, Xue YJ, Zhu Y, Zhao JM, Li M, Zhang J, Wu JY, Lei F, Wang QY, Li S, Zheng CP, Ai B, Tang ZD, Feng CC, Liao LD, Wang SH, Shen JH, Liu YJ, Bai XF, He JZ, Cao HH, Wu BL, Wang MR, Lin DC, Koeffler HP, Wang LD, Li X, Li EM, Xu LY. Integrative analyses of transcriptome sequencing identify novel functional lncRNAs in esophageal squamous cell carcinoma. Oncogenesis 2017; 6:e297. [PMID: 28194033 PMCID: PMC5337622 DOI: 10.1038/oncsis.2017.1] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 12/17/2016] [Accepted: 12/23/2016] [Indexed: 02/05/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have a critical role in cancer initiation and progression, and thus may mediate oncogenic or tumor suppressing effects, as well as be a new class of cancer therapeutic targets. We performed high-throughput sequencing of RNA (RNA-seq) to investigate the expression level of lncRNAs and protein-coding genes in 30 esophageal samples, comprised of 15 esophageal squamous cell carcinoma (ESCC) samples and their 15 paired non-tumor tissues. We further developed an integrative bioinformatics method, denoted URW-LPE, to identify key functional lncRNAs that regulate expression of downstream protein-coding genes in ESCC. A number of known onco-lncRNA and many putative novel ones were effectively identified by URW-LPE. Importantly, we identified lncRNA625 as a novel regulator of ESCC cell proliferation, invasion and migration. ESCC patients with high lncRNA625 expression had significantly shorter survival time than those with low expression. LncRNA625 also showed specific prognostic value for patients with metastatic ESCC. Finally, we identified E1A-binding protein p300 (EP300) as a downstream executor of lncRNA625-induced transcriptional responses. These findings establish a catalog of novel cancer-associated functional lncRNAs, which will promote our understanding of lncRNA-mediated regulation in this malignancy.
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Affiliation(s)
- C-Q Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - G-W Huang
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - Z-Y Wu
- Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, China
| | - Y-J Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - X-C Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Y-J Xue
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - Y Zhu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - J-M Zhao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - M Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - J Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - J-Y Wu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - F Lei
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - Q-Y Wang
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - S Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - C-P Zheng
- Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, China
| | - B Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Z-D Tang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - C-C Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - L-D Liao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - S-H Wang
- Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, China
| | - J-H Shen
- Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, China
| | - Y-J Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - X-F Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - J-Z He
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - H-H Cao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - B-L Wu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - M-R Wang
- Cancer Institute/Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - D-C Lin
- Division of Hematology/Oncology, Cedars-Sinai Medical Center, University of California, Los Angeles School of Medicine, Los Angeles, CA, USA
| | - H P Koeffler
- Division of Hematology/Oncology, Cedars-Sinai Medical Center, University of California, Los Angeles School of Medicine, Los Angeles, CA, USA
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- National University Cancer Institute of Singapore, National University Health System and National University Hospital, Singapore, Singapore
| | - L-D Wang
- Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - X Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. E-mail:
| | - E-M Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong 515041, China. E-mail:
| | - L-Y Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong 515041, China. E-mail:
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193
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Gu C, Liao B, Li X, Cai L, Chen H, Li K, Yang J. Network-based collaborative filtering recommendation model for inferring novel disease-related miRNAs. RSC Adv 2017. [DOI: 10.1039/c7ra09229f] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
According to the miRNA and disease similarity network, the unknown associations are predicted by combining the known miRNA-disease association network based on collaborative filtering recommendation algorithm.
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Affiliation(s)
- Changlong Gu
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Bo Liao
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Xiaoying Li
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Lijun Cai
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Haowen Chen
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Keqin Li
- Department of Computer Science
- State University of New York
- New York 12561
- USA
| | - Jialiang Yang
- Department of Genetics and Gnomic Science
- Icahn School of Medicine at Mount Sinai
- New York 10029
- USA
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194
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Wen J, Tan Y, Jiang L. A Reconfiguration Strategy of Distribution Networks Considering Node Importance. PLoS One 2016; 11:e0168350. [PMID: 27992589 PMCID: PMC5167376 DOI: 10.1371/journal.pone.0168350] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 11/29/2016] [Indexed: 12/13/2022] Open
Abstract
Node importance degree is a vital index in distribution network reconfiguration because it reflects the robustness of the network structure by evaluating node importance. Since the traditional reconfiguration ignores this index, the reconstructed network structure may be vulnerable which would reduce the security and stability of the distribution systems. This paper presents a novel reconfiguration strategy considering the node importance. The optimization objectives are the improvement of the node importance degree and the reduction of power loss. To balance the objectives, the reconfiguration mathematical model is formulated as a compound objective function with weight coefficients. Then the quantum particle swarm algorithm is employed to address this compound objective optimization problem. The strategy can model different scenarios network reconfiguration by adjusting the weight vector based on the tendencies of the utility decision maker. Illustrative examples verify the effectiveness of the proposed strategy.
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Affiliation(s)
- Juan Wen
- College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China
- College of Electrical and Information Engineering, University of south China, Hengyang, Hunan, China
| | - Yanghong Tan
- College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China
| | - Lin Jiang
- College of Electrical and Information Engineering, University of south China, Hengyang, Hunan, China
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195
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Li YH, Wang PP, Li XX, Yu CY, Yang H, Zhou J, Xue WW, Tan J, Zhu F. The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective. PLoS One 2016; 11:e0165737. [PMID: 27828998 PMCID: PMC5102354 DOI: 10.1371/journal.pone.0165737] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/17/2016] [Indexed: 11/18/2022] Open
Abstract
The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.
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Affiliation(s)
- Ying Hong Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Pan Pan Wang
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Xiao Xu Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Chun Yan Yu
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Hong Yang
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Jin Zhou
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Wei Wei Xue
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Jun Tan
- Institute of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
- * E-mail:
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196
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Wang J. A Survey of Methods for Constructing Rooted Phylogenetic Networks. PLoS One 2016; 11:e0165834. [PMID: 27806124 PMCID: PMC5091748 DOI: 10.1371/journal.pone.0165834] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 10/18/2016] [Indexed: 11/18/2022] Open
Abstract
Rooted phylogenetic networks are primarily used to represent conflicting evolutionary information and describe the reticulate evolutionary events in phylogeny. So far a lot of methods have been presented for constructing rooted phylogenetic networks, of which the methods based on the decomposition property of networks and by means of the incompatible graph (such as the CASS, the LNETWORK and the BIMLR) are more efficient than other available methods. The paper will discuss and compare these methods by both the practical and artificial datasets, in the aspect of the running time of the methods and the effective of constructed phylogenetic networks. The results show that the LNETWORK can construct much simper networks than the others.
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Affiliation(s)
- Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, China
- * E-mail:
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197
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Gu C, Liao B, Li X, Li K. Network Consistency Projection for Human miRNA-Disease Associations Inference. Sci Rep 2016; 6:36054. [PMID: 27779232 PMCID: PMC5078764 DOI: 10.1038/srep36054] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/11/2016] [Indexed: 11/20/2022] Open
Abstract
Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases. NCPMDA is a non-parametric universal network-based method that can simultaneously predict miRNA-disease associations in all diseases but does not require negative samples. NCPMDA can also confirm the presence of miRNAs in isolated diseases (diseases without any known miRNA association). Leave-one-out cross validation and case studies have shown that the predictive performance of NCPMDA is superior over that of previous method.
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Affiliation(s)
- Changlong Gu
- College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Bo Liao
- College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Xiaoying Li
- College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Keqin Li
- Department of Computer Science, State University of New York, New Paltz, New York 12561, USA
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198
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Zhao J, Song X, Wang K. lncScore: alignment-free identification of long noncoding RNA from assembled novel transcripts. Sci Rep 2016; 6:34838. [PMID: 27708423 PMCID: PMC5052565 DOI: 10.1038/srep34838] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 09/21/2016] [Indexed: 12/21/2022] Open
Abstract
RNA-Seq based transcriptome assembly has been widely used to identify novel lncRNAs. However, the best-performing transcript reconstruction methods merely identified 21% of full-length protein-coding transcripts from H. sapiens. Those partial-length protein-coding transcripts are more likely to be classified as lncRNAs due to their incomplete CDS, leading to higher false positive rate for lncRNA identification. Furthermore, potential sequencing or assembly error that gain or abolish stop codons also complicates ORF-based prediction of lncRNAs. Therefore, it remains a challenge to identify lncRNAs from the assembled transcripts, particularly the partial-length ones. Here, we present a novel alignment-free tool, lncScore, which uses a logistic regression model with 11 carefully selected features. Compared to other state-of-the-art alignment-free tools (e.g. CPAT, CNCI, and PLEK), lncScore outperforms them on accurately distinguishing lncRNAs from mRNAs, especially partial-length mRNAs in the human and mouse datasets. In addition, lncScore also performed well on transcripts from five other species (Zebrafish, Fly, C. elegans, Rat, and Sheep). To speed up the prediction, multithreading is implemented within lncScore, and it only took 2 minute to classify 64,756 transcripts and 54 seconds to train a new model with 21,000 transcripts with 12 threads, which is much faster than other tools. lncScore is available at https://github.com/WGLab/lncScore.
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Affiliation(s)
- Jian Zhao
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Xiaofeng Song
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Kai Wang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA
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199
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OAHG: an integrated resource for annotating human genes with multi-level ontologies. Sci Rep 2016; 6:34820. [PMID: 27703231 PMCID: PMC5050487 DOI: 10.1038/srep34820] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 09/20/2016] [Indexed: 01/04/2023] Open
Abstract
OAHG, an integrated resource, aims to establish a comprehensive functional annotation resource for human protein-coding genes (PCGs), miRNAs, and lncRNAs by multi-level ontologies involving Gene Ontology (GO), Disease Ontology (DO), and Human Phenotype Ontology (HPO). Many previous studies have focused on inferring putative properties and biological functions of PCGs and non-coding RNA genes from different perspectives. During the past several decades, a few of databases have been designed to annotate the functions of PCGs, miRNAs, and lncRNAs, respectively. A part of functional descriptions in these databases were mapped to standardize terminologies, such as GO, which could be helpful to do further analysis. Despite these developments, there is no comprehensive resource recording the function of these three important types of genes. The current version of OAHG, release 1.0 (Jun 2016), integrates three ontologies involving GO, DO, and HPO, six gene functional databases and two interaction databases. Currently, OAHG contains 1,434,694 entries involving 16,929 PCGs, 637 miRNAs, 193 lncRNAs, and 24,894 terms of ontologies. During the performance evaluation, OAHG shows the consistencies with existing gene interactions and the structure of ontology. For example, terms with more similar structure could be associated with more associated genes (Pearson correlation γ2 = 0.2428, p < 2.2e-16).
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200
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Differential network analysis from cross-platform gene expression data. Sci Rep 2016; 6:34112. [PMID: 27677586 PMCID: PMC5039701 DOI: 10.1038/srep34112] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 09/07/2016] [Indexed: 01/18/2023] Open
Abstract
Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes.
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