Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk

Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.

[1]  Seung Yong Hwang,et al.  MicroRNA and gene expression analysis of melatonin‐exposed human breast cancer cell lines indicating involvement of the anticancer effect , 2011, Journal of pineal research.

[2]  Yufei Huang,et al.  Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors , 2013, PloS one.

[3]  Yusuke Yamamoto,et al.  An integrative genomic analysis revealed the relevance of microRNA and gene expression for drug-resistance in human breast cancer cells , 2011, Molecular Cancer.

[4]  H. Grosshans,et al.  Active turnover modulates mature microRNA activity in Caenorhabditis elegans , 2009, Nature.

[5]  Sandro Banfi,et al.  microRNAs and genetic diseases , 2009, PathoGenetics.

[6]  Yi Pan,et al.  Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[7]  Q. Zou,et al.  Similarity computation strategies in the microRNA-disease network: a survey. , 2015, Briefings in functional genomics.

[8]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  J. Cerhan,et al.  Gene networks and microRNAs implicated in aggressive prostate cancer. , 2009, Cancer research.

[10]  Yang Li,et al.  HMDD v2.0: a database for experimentally supported human microRNA and disease associations , 2013, Nucleic Acids Res..

[11]  Nicholas Bertos,et al.  miR-378(∗) mediates metabolic shift in breast cancer cells via the PGC-1β/ERRγ transcriptional pathway. , 2010, Cell metabolism.

[12]  Hailong Wu,et al.  Suppression of cell growth and invasion by miR-205 in breast cancer , 2008, Cell Research.

[13]  Cheng Liang,et al.  Predicting MicroRNA-Disease Associations Using Kronecker Regularized Least Squares Based on Heterogeneous Omics Data , 2017, IEEE Access.

[14]  Xiangxiang Zeng,et al.  Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  W. Ritchie,et al.  Predicting microRNA targets and functions: traps for the unwary , 2009, Nature Methods.

[16]  Xing Chen,et al.  Semi-supervised learning for potential human microRNA-disease associations inference , 2014, Scientific Reports.

[17]  Yadong Wang,et al.  Prioritization of disease microRNAs through a human phenome-microRNAome network , 2010, BMC Systems Biology.

[18]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[19]  M. Wirth,et al.  Elevated expression of prostate cancer-associated genes is linked to down-regulation of microRNAs , 2014, BMC Cancer.

[20]  Xia Li,et al.  Prediction of potential disease-associated microRNAs based on random walk , 2015, Bioinform..

[21]  Eckart Meese,et al.  MicroRNAs – Important Molecules in Lung Cancer Research , 2011, Front. Gene..

[22]  Xing Chen,et al.  RWRMDA: predicting novel human microRNA-disease associations. , 2012, Molecular bioSystems.

[23]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[24]  Changning Liu,et al.  dbDEMC: a database of differentially expressed miRNAs in human cancers , 2010, BMC Genomics.

[25]  Jiuyong Li,et al.  Identifying miRNAs, targets and functions , 2012, Briefings Bioinform..

[26]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[27]  Xia Li,et al.  Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes , 2013, BMC Systems Biology.

[28]  E. Miska,et al.  MicroRNA functions in animal development and human disease , 2005, Development.

[29]  N. Lynam‐Lennon,et al.  The roles of microRNA in cancer and apoptosis , 2009, Biological reviews of the Cambridge Philosophical Society.

[30]  Geoffrey E. Hinton,et al.  A Scalable Hierarchical Distributed Language Model , 2008, NIPS.

[31]  Jiawei Luo,et al.  A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network , 2017, J. Biomed. Informatics.

[32]  Hyeon-Eui Kim,et al.  Deep mining heterogeneous networks of biomedical linked data to predict novel drug‐target associations , 2017, Bioinform..

[33]  Cheng Liang,et al.  Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[34]  Xing Chen,et al.  HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction , 2016, Oncotarget.

[35]  Qionghai Dai,et al.  WBSMDA: Within and Between Score for MiRNA-Disease Association prediction , 2016, Scientific Reports.

[36]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[37]  Ming You,et al.  MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer. , 2012, Carcinogenesis.

[38]  Qionghai Dai,et al.  RBMMMDA: predicting multiple types of disease-microRNA associations , 2015, Scientific Reports.

[39]  Elena Marchiori,et al.  Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..

[40]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[41]  Sambasivarao Damaraju,et al.  Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer , 2015, BMC Genomics.

[42]  Xiangxiang Zeng,et al.  Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks , 2016, Briefings Bioinform..

[43]  Fabian J Theis,et al.  PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes , 2010, Genome Biology.