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Network-based method for mining novel HPV infection related genes using random walk with restart algorithm

BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE 2018年第6期1864卷 2376-2383页

作者： Zhu, Liucun Su, Fangchu Xu, YaoChen Zou, Quan Shanghai Univ Sch Life Sci Shanghai 200444 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Biochem & Cell Biol Shanghai 200031 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China

The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

关键词： Human papillomavirus Protein-protein interaction random walk with restart algorithm GO terms KEGG pathways

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Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms

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BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE 2018年第6期1864卷 2228-2240页

作者： Chen, Lei Liu, Tao Zhao, Xian Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

关键词： Anatomical therapeutic chemical Chemical chemical interaction Shortest path algorithm random walk with restart algorithm

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GPEC: A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2012年 37卷 17-23页

作者： Duc-Hau Le Kwon, Yung-Keun Univ Ulsan Sch Elect Engn Complex Syst Comp Lab Ulsan 680749 South Korea Water Resources Univ Sch Comp Sci & Engn Hanoi Vietnam

Finding genes associated with a disease is an important issue in the biomedical area and many gene prioritization methods have been proposed for this goal. Among these, network-based approaches are recently proposed and outperformed functional annotation-based ones. Here, we introduce a novel Cytoscape plug-in, GPEC, to help identify putative genes likely to be associated with specific diseases or pathways. In the plug-in, gene prioritization is performed through a random walk with restart algorithm, a state-of-the art network-based method, along with a gene/protein relationship network. The plug-in also allows users efficiently collect biomedical evidence for highly ranked candidate genes. A set of known genes, candidate genes and a gene/protein relationship network can be provided in a flexible way. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Cytoscape plug-in Gene prioritization random walk with restart algorithm Biomedical evidence collection

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Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information

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BMC BIOINFORMATICS 2019年第1期20卷 1-12页

作者： Fan, Xiao-Nan Zhang, Shao-Wu Zhang, Song-Yao Zhu, Kunju Lu, Songjian Northwestern Polytech Univ Key Lab Informat Fus Technol Minist Educ Sch Automat 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Univ Pittsburgh Dept Biomed Informat 5607 Baum Blvd Pittsburgh PA 15206 USA Jinan Univ Affiliated Hosp 1 Guangzhou Guangdong Peoples R China Jinan Univ Clin Med Res Inst Guangzhou Guangdong Peoples R China

BackgroundLong non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time *** this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and random walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease *** with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://***/NWPU-903PR/IDHI-MIRW.

关键词： Long noncoding RNA Disease lncRNA-disease association Heterogeneous network random walk with restart algorithm

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Identifying Pathological Myopia Associated Genes with A random walk-Based Method in Protein-Protein Interaction Network

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CURRENT BIOINFORMATICS 2024年第4期19卷 375-384页

作者： Zhang, Jiyu Huang, Tao Sun, Qiao Zhang, Jian Shanghai Gen Hosp Dept Ophthalmol Shanghai Peoples R China Natl Clin Res Ctr Eye Dis Shanghai Peoples R China Shanghai Clin Res Ctr Eye Dis Shanghai Peoples R China Shanghai Municipal Key Clin Specialty Shanghai Peoples R China Shanghai Key Lab Ocular Fundus Dis Shanghai Peoples R China Shanghai Engn Ctr Visual Sci & Photomed Shanghai Peoples R China Shanghai Engn Ctr Precise Diag & Treatment Eye Dis Shanghai Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Shanghai Inst Nutr & Hlth CAS Key Lab Computat BiolBiomed Big Data Ctr Shanghai Peoples R China Chinese Acad Sci Univ Chinese Acad Sci CAS Key Lab Tissue Microenvironm & Tumor Shanghai Inst Nutr & Hlth Shanghai Peoples R China Shanghai Aier Eye Hosp Shanghai Aier Eye Inst Dept Ophthalmol Shanghai Peoples R China

Background Pathological myopia, a severe variant of myopia, extends beyond the typical refractive error associated with nearsightedness. While the condition has a strong genetic component, the intricate mechanisms of inheritance remain elusive. Some genes have been associated with the development of pathological myopia, but their exact roles are not fully *** This study aimed to identify novel genes associated with pathological *** Our study leveraged DisGeNET to identify 184 genes linked with high myopia and 39 genes related to degenerative myopia. To uncover additional pathological myopia-associated genes, we employed the random walk with restart algorithm to investigate the protein-protein interactions network. We used the previously identified 184 high myopia and 39 degenerative myopia genes as seed *** Through subsequent screening tests, we discarded genes with weak associations, yielding 103 new genes for high myopia and 33 for degenerative *** We confirmed the association of certain genes, including six genes that were confirmed to be associated with both high and degenerative myopia. The newly discovered genes are helpful to uncover and understand the pathogenesis of myopia.

关键词： Pathological myopia high myopia degenerative myopia random walk with restart algorithm protein-protein interaction network screening test

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Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2013年 44卷 1-8页

作者： Le, Duc-Hau Kwon, Yung-Keun Univ Ulsan Sch Elect Engn Ulsan 780749 South Korea Water Resources Univ Sch Comp Sci & Engn Hanoi Vietnam

Background: Finding candidate genes associated with a disease is an important issue in biomedical research. Recently, many network-based methods have been proposed that implicitly utilize the modularity principle, which states that genes causing the same or similar diseases tend to form physical or functional modules in gene/protein relationship networks. Of these methods, the random walk with restart (RWR) algorithm is considered to be a state-of-the-art approach, but the modularity principle has not been fully considered in traditional RWR approaches. Therefore, we propose a novel method called ORIENT (neighbor-favoring weight reinforcement) to improve the performance of RWR through proper intensification of the weights of interactions close to the known disease genes. Results: Through extensive simulations over hundreds of diseases, we observed that our approach performs better than the traditional RWR algorithm. In particular, our method worked best when the weights of interactions involving only the nearest neighbor genes of the disease genes were intensified. Interestingly, the performance of our approach was negatively related to the probability with which the random walk will restart, whereas the performance of RWR without the weight-reinforcement was positively related in dense gene/protein relationship networks. We further found that the density of the disease gene-projected sub-graph and the number of paths between the disease genes in a gene/protein relationship network may be explanatory variables for the RWR performance. Finally, a comparison with other well-known gene prioritization tools including Endeavour, ToppGene, and BioGraph, revealed that our approach shows significantly better performance. Conclusion: Taken together, these findings provide insight to efficiently guide RWR in disease gene prioritization. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Gene prioritization random walk with restart algorithm Weight reinforcement

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Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network

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BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE 2018年第6期1864卷 2284-2293页

作者： Yuan, Fei Lu, WenCong Binzhou Med Univ Hosp Dept Sci & Technol Binzhou 256603 Shandong Peoples R China Shanghai Univ Dept Chem Shanghai 200072 Peoples R China

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. "Cancer driver" theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

关键词： Lung adenocarcinoma Driver genes random walk with restart algorithm Protein-protein interaction network

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Disease phenotype similarity improves the prediction of novel disease-associated microRNAs

Disease phenotype similarity improves the prediction of nove...

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2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS 2015)

作者： Duc-Hau Le Water Resources Univ Sch Comp Sci & Engn 175 Tay Son Hanoi Vietnam

ISBN: (纸本)9781467366403

Many studies have shown roles of miRNAs (microRNAs) on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, network-based methods are becoming dominant since they well exploit the "disease module" principle in miRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method on a miRNA functional similarity network, namely RWRMDA, is state-of-the-art one. The use of this algorithm was inspired from its success in predicting disease gene because "disease module" principle also exists in protein interaction networks. Besides, many other algorithms were also designed for prediction of disease genes. However, they have not yet been utilized for disease microRNA prediction. In this study, we proposed a method, namely RWRHMDA, for prediction of disease-associated miRNAs. This method was based on RWRH algorithm, which was successfully proposed for disease gene prediction on a heterogeneous network of genes and disease phenotypes. In particular, we used this algorithm to rank disease candidate miRNAs on a heterogeneous network of phenotypes and miRNAs, which was constructed by integrating a shared target gene-based microRNA functional similarity network and a disease phenotype similarity network. Comparing the prediction performance of RWRHMDA with that of RWRMDA on a set of 35 disease phenotypes, we found that RWRHMDA significantly outperformed RWRMDA irrespective of parameter settings since it better exploited "disease module" principle. In addition, using RWRHMDA method, we identified eight novel Alzheimer's disease-associated miRNAs.

关键词： disease-associated miRNA random walk with restart algorithm RWRH Alzheimer's disease

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MCRWR: a new method to measure the similarity of documents based on semantic network

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BMC BIOINFORMATICS 2022年第1期23卷 56-56页

作者： Pan, Xianwei Huang, Peng Li, Shan Cui, Lei China Med Univ Sch Hlth Management 77 Puhe Rd Shenyang Liaoning Peoples R China

Background Besides Boolean retrieval with medical subject headings (MeSH), PubMed provides users with an alternative way called "Related Articles" to access and collect relevant documents based on semantic similarity. To explore the functionality more efficiently and more accurately, we proposed an improved algorithm by measuring the semantic similarity of PubMed citations based on the MeSH-concept network model. Results Three article similarity networks are obtained using MeSH-concept random walk with restart (MCRWR), MeSH random walk with restart (MRWR) and PubMed related article (PMRA) respectively. The area under receiver operating characteristic (ROC) curve of MCRWR, MRWR and PMRA is 0.93, 0.90, and 0.67 respectively. Precisions of MCRWR and MRWR under various similarity thresholds are higher than that of PMRA. Mean value of P5 of MCRWR is 0.742, which is much higher than those of MRWR (0.692) and PMRA (0.223). In the article semantic similarity network of "Genes & Function of organ & Disease" based on MCRWR algorithm, four topics are identified according to golden standards. Conclusion MeSH-concept random walk with restart algorithm has better performance in constructing article semantic similarity network, which can reveal the implicitly semantic association between documents. The efficiency and accuracy of retrieving semantic-related documents have been improved a lot.

关键词： Semantic similarity network Network analysis Medical subject headings random walk with restart algorithm

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MRWMDA: A novel framework to infer miRNA-disease associations

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BIOSYSTEMS 2021年 199卷 104292-104292页

作者： Wang Meixi Zhu Ping Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China

MicroRNAs (miRNAs) are widely involved in a series of significant biological processes, which have been revealed and verified by accumulating experimental studies. The computational inference of the correlation between miRNAs and diseases is essential to facilitate the detection of disease biomarkers for disease diagnosis, prevention, treatment and prognosis. In this paper, a model with Multiple use of random walk with restart algorithm was introduced for the prediction of the MiRNA-Disease Association (MRWMDA). Based on diverse similarity measures, the model first implemented the random walk with restart (RWR) algorithm on the integrated similarity network to construct the topological similarity of miRNAs and diseases, which took full advantage of the network topology information. Then, the RWR algorithm was applied in the miRNA topological similarity network, and a steady probability of each miRNA-disease pair was obtained to prioritize miRNA candidates. In particular, the initial probability of the RWR algorithm was determined by utilizing the combination of the recommendation algorithm and the maximum similarity method. The proposed model achieved significant improvement in prediction compared with previous models, with an AUC of 0.9353 and an AUPR of 0.4809. In addition, case studies of breast neoplasms and lung neoplasms representing different disease types further demonstrated the excellent ability of MRWMDA in detecting potential disease-associated miRNAs. These performance analyses indicated that MRWMDA could be an effective and powerful biological computational tool in relevant biomedical studies.

关键词： miRNA Disease Association prediction random walk with restart algorithm

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