Predicting miRNA‐disease association based on inductive matrix completion

Motivation: It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. Results: This paper presents a novel model of Inductive Matrix Completion for MiRNA‐Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA‐disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave‐one‐out‐cross‐validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms‐related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms‐related miRNAs are verified. Availability and implementation: The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  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.

[2]  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.

[3]  Ana Kozomara,et al.  miRBase: integrating microRNA annotation and deep-sequencing data , 2010, Nucleic Acids Res..

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

[5]  Xing Chen,et al.  MCMDA: Matrix completion for MiRNA-disease association prediction , 2017, Oncotarget.

[6]  Carlos Caldas,et al.  Biological and prognostic associations of miR‐205 and let‐7b in breast cancer revealed by in situ hybridization analysis of micro‐RNA expression in arrays of archival tumour tissue , 2012, The Journal of pathology.

[7]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[8]  Xing Chen,et al.  PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction , 2017, PLoS Comput. Biol..

[9]  David P. Bartel,et al.  Supporting Online Material Materials and Methods Fig. S1 Tables S1 and S2 References Database S1 Disrupting the Pairing between Let-7 and Hmga2 Enhances Oncogenic Transformation , 2022 .

[10]  John O. Woods,et al.  Correction: Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses , 2013, PLoS ONE.

[11]  Ana Kozomara,et al.  miRBase: annotating high confidence microRNAs using deep sequencing data , 2013, Nucleic Acids Res..

[12]  V. Ambros.  The functions of animal microRNAs , 2004, Nature.

[13]  E. Wang,et al.  Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. , 2014, Seminars in cancer biology.

[14]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[15]  C. Pasquier,et al.  Prediction of miRNA-disease associations with a vector space model , 2016, Scientific Reports.

[16]  Gang Lu,et al.  Loss of Brain-enriched miR-124 MicroRNA Enhances Stem-like Traits and Invasiveness of Glioma Cells* , 2012, The Journal of Biological Chemistry.

[17]  Jean-Philippe Vert,et al.  ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples , 2011, BMC Bioinformatics.

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

[19]  Xing Chen,et al.  NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations , 2017, Database J. Biol. Databases Curation.

[20]  J. E. Kim,et al.  microRNAs in breast cancer: regulatory roles governing the hallmarks of cancer , 2016, Biological reviews of the Cambridge Philosophical Society.

[21]  Zuping Zhang,et al.  miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships , 2017, Scientific Reports.

[22]  Xing Chen,et al.  RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction , 2017, RNA biology.

[23]  Ujjwal Maulik,et al.  Development of the human cancer microRNA network , 2010 .

[24]  V. Ambros,et al.  The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 , 1993, Cell.

[25]  T. Tuschl,et al.  Mechanisms of gene silencing by double-stranded RNA , 2004, Nature.

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

[27]  Xing Chen,et al.  Long non-coding RNAs and complex diseases: from experimental results to computational models , 2016, Briefings Bioinform..

[28]  Wenli Wan,et al.  Research progress of Hodgkin lymphoma , 2014 .

[29]  Punam Pahwa,et al.  Clustering of cancer among families of cases with Hodgkin Lymphoma (HL), Multiple Myeloma (MM), Non-Hodgkin's Lymphoma (NHL), Soft Tissue Sarcoma (STS) and control subjects , 2009, BMC Cancer.

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

[31]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

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

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

[34]  Yongdong Zhang,et al.  Drug-target interaction prediction: databases, web servers and computational models , 2016, Briefings Bioinform..

[35]  John O. Woods,et al.  Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses , 2013, PloS one.

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

[37]  M. Nickerson,et al.  MicroRNA-23b* targets proline oxidase, a mitochondrial tumor suppressor protein in renal cancer , 2010, Oncogene.

[38]  Q. Cui,et al.  An Analysis of Human MicroRNA and Disease Associations , 2008, PloS one.

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

[40]  Xing Chen,et al.  MicroRNAs and complex diseases: from experimental results to computational models , 2019, Briefings Bioinform..

[41]  Rick van der Zwan,et al.  Medication Adherence in Patients with Rheumatoid Arthritis: The Effect of Patient Education, Health Literacy, and Musculoskeletal Ultrasound , 2015, BioMed research international.

[42]  Nagarajan Natarajan,et al.  Inductive matrix completion for predicting gene–disease associations , 2014, Bioinform..

[43]  Jan Gorodkin,et al.  Protein-driven inference of miRNA–disease associations , 2013, Bioinform..

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

[45]  Renato Baserga,et al.  Micro RNA 145 Targets the Insulin Receptor Substrate-1 and Inhibits the Growth of Colon Cancer Cells* , 2007, Journal of Biological Chemistry.

[46]  Yun Xiao,et al.  Prioritizing Candidate Disease miRNAs by Topological Features in the miRNA Target–Dysregulated Network: Case Study of Prostate Cancer , 2011, Molecular Cancer Therapeutics.

[47]  V. Ambros.  microRNAs Tiny Regulators with Great Potential , 2001, Cell.

[48]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

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

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

[51]  C. Croce,et al.  MicroRNA signatures in human cancers , 2006, Nature Reviews Cancer.

[52]  P. Sarnow,et al.  Modulation of Hepatitis C Virus RNA Abundance by a Liver-Specific MicroRNA , 2005, Science.

[53]  Gerald Hoefler,et al.  miR-192, miR-194, miR-215, miR-200c and miR-141 are downregulated and their common target ACVR2B is strongly expressed in renal childhood neoplasms. , 2012, Carcinogenesis.

[54]  Naoto Tsuchiya,et al.  Circulating Exosomal microRNAs as Biomarkers of Colon Cancer , 2014, PloS one.

[55]  Inderjit S. Dhillon,et al.  Provable Inductive Matrix Completion , 2013, ArXiv.

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

[57]  Q. Zou,et al.  Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods , 2015, BioMed research international.

[58]  Dong Wang,et al.  Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases , 2010, Bioinform..

[59]  M. Byrom,et al.  Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis , 2005, Nucleic acids research.

[60]  C E Lipscomb,et al.  Medical Subject Headings (MeSH). , 2000, Bulletin of the Medical Library Association.

[61]  E. Miska,et al.  How microRNAs control cell division, differentiation and death. , 2005, Current opinion in genetics & development.

[62]  Xiangxiang Zeng,et al.  Prediction of potential disease-associated microRNAs using structural perturbation method , 2017, bioRxiv.

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

[64]  Jinfeng Zou,et al.  Identification and Construction of Combinatory Cancer Hallmark-Based Gene Signature Sets to Predict Recurrence and Chemotherapy Benefit in Stage II Colorectal Cancer. , 2016, JAMA oncology.

[65]  E. Wiemer.  The role of microRNAs in cancer: no small matter. , 2007, European journal of cancer.

[66]  A. Jemal,et al.  Cancer Statistics, 2006 , 2006, CA: a cancer journal for clinicians.

[67]  Xantha Karp,et al.  Encountering MicroRNAs in Cell Fate Signaling , 2005, Science.

[68]  C. Croce,et al.  miR-15 and miR-16 induce apoptosis by targeting BCL2. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[69]  Li-ping Guo,et al.  The oncogenetic role of microRNA-31 as a potential biomarker in oesophageal squamous cell carcinoma. , 2011, Clinical science.

[70]  Xiaojun Chen,et al.  Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm , 2017, Scientific Reports.

[71]  Jeffrey S. Harrison,et al.  Leukemia and Lymphoma Society , 2018, The Grants Register 2019.

[72]  A. Jemal,et al.  Cancer statistics, 2012 , 2012, CA: a cancer journal for clinicians.

[73]  Raquel Díaz,et al.  Deregulated expression of miR‐106a predicts survival in human colon cancer patients , 2008, Genes, chromosomes & cancer.

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