Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA–Disease–Drug–Protein Graph

One key issue in the post-genomic era is how to systematically describe the associations between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens the possibility of investigating the relationships between different molecules from a global perspective. In this article, a molecular association network (MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attribute of the node itself (e.g., sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the behavior of the node in the complex network (associations with other nodes). A random forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieved a superb performance with an area under curve (AUC) of 0.9735 under a five-fold cross-validation, which showed that the proposed method could provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

[1]  Juwen Shen,et al.  Predicting protein–protein interactions based only on sequences information , 2007, Proceedings of the National Academy of Sciences.

[2]  Alex W. Wilkinson,et al.  Computational prediction of protein-protein interactions , 2012 .

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

[4]  Xiao Li,et al.  A High Efficient Biological Language Model for Predicting Protein–Protein Interactions , 2019, Cells.

[5]  Lei Wang,et al.  BNPMDA: Bipartite Network Projection for MiRNA–Disease Association prediction , 2018, Bioinform..

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

[7]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[8]  Sebo Withoff,et al.  Genetic variation in the non-coding genome: Involvement of micro-RNAs and long non-coding RNAs in disease. , 2014, Biochimica et biophysica acta.

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

[10]  Sean R. Collins,et al.  Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S , 2007, Molecular & Cellular Proteomics.

[11]  Hai-Cheng Yi,et al.  A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information , 2018, Molecular therapy. Nucleic acids.

[12]  Zhu-Hong You,et al.  RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information. , 2016, Current protein & peptide science.

[13]  Xing Chen,et al.  Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA , 2015, Scientific Reports.

[14]  Michael Q. Zhang,et al.  NONCODEV5: a comprehensive annotation database for long non-coding RNAs , 2017, Nucleic Acids Res..

[15]  Qinghua Guo,et al.  LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse , 2018, Nucleic Acids Res..

[16]  V. Ambros MicroRNA Pathways in Flies and Worms Growth, Death, Fat, Stress, and Timing , 2003, Cell.

[17]  Ana Kozomara,et al.  miRBase: from microRNA sequences to function , 2018, Nucleic Acids Res..

[18]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[19]  Damian Szklarczyk,et al.  The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible , 2016, Nucleic Acids Res..

[20]  Núria Queralt-Rosinach,et al.  DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants , 2016, Nucleic Acids Res..

[21]  Yue Zhao,et al.  MNDR v2.0: an updated resource of ncRNA–disease associations in mammals , 2017, Nucleic Acids Res..

[22]  Xing Chen,et al.  LncRNADisease: a database for long-non-coding RNA-associated diseases , 2012, Nucleic Acids Res..

[23]  C. Ponting,et al.  Evolution and Functions of Long Noncoding RNAs , 2009, Cell.

[24]  S. Fields,et al.  A novel genetic system to detect protein–protein interactions , 1989, Nature.

[25]  Zhu-Hong You,et al.  A heterogeneous label propagation approach to explore the potential associations between miRNA and disease , 2018, Journal of Translational Medicine.

[26]  Yuan Zhou,et al.  HMDD v3.0: a database for experimentally supported human microRNA–disease associations , 2018, Nucleic Acids Res..

[27]  Qiong Zhang,et al.  lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs , 2017, Nucleic Acids Res..

[28]  Hsien-Da Huang,et al.  miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions , 2017, Nucleic Acids Res..

[29]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[30]  Zhu-Hong You,et al.  Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis , 2013, BMC Bioinformatics.

[31]  B. Yan,et al.  The research strategies for probing the function of long noncoding RNAs. , 2012, Genomics.

[32]  T. Ashburn,et al.  Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.

[33]  Bolin Chen,et al.  A learning-based framework for miRNA-disease association identification using neural networks , 2018, bioRxiv.

[34]  Hai-Cheng Yi,et al.  ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation , 2019, Molecular therapy. Nucleic acids.

[35]  L. Bonetta Protein–protein interactions: Interactome under construction , 2010, Nature.

[36]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2019 , 2018, Nucleic Acids Res..