Plant miRNA function prediction based on functional similarity network and transductive multi-label classification algorithm

Plant miRNAs play critical roles in the response to abiotic and biotic stress. The advancement in the number of plant miRNA functions lags far behind that of plant miRNAs. In this paper, a method to predict the functions of plant miRNAs is proposed. The functional similarity between each pair of miRNAs is inferred based on a weighted protein-protein interaction network (WPPIN) and graph-theoretic properties. A miRNA functional similarity network (MFSN) is constructed by a simple but robust rank-based approach. Transductive multi-label classification (TRAM) is applied to the MFSN. The experimental results demonstrate that our prediction approach obtains high effectiveness in Arabidopsis thaliana. It can also be applied to other plant species when protein-protein interaction networks of various organisms are available. Weight of protein-protein interaction network is calculated by using the similarity of GO terms.MiRNA functional similarity network is constructed by rank-based method.Prediction method of miRNA functions based on similarity network and transductive multi-label classification is proposed.

[1]  Patrick Xuechun Zhao,et al.  psRNATarget: a plant small RNA target analysis server , 2011, Nucleic Acids Res..

[2]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[3]  Tao Mei,et al.  Correlative multi-label video annotation , 2007, ACM Multimedia.

[4]  Jian-Kang Zhu,et al.  A miRNA Involved in Phosphate-Starvation Response in Arabidopsis , 2005, Current Biology.

[5]  Limsoon Wong,et al.  Exploiting indirect neighbours and topological weight to predict protein function from protein--protein interactions , 2006 .

[6]  V. Ambros.  A hierarchy of regulatory genes controls a larva-to-adult developmental switch in C. elegans , 1989, Cell.

[7]  Xiaoping Zhou,et al.  The Predicted Arabidopsis Interactome Resource and Network Topology-Based Systems Biology Analyses[W][OA] , 2011, Plant Cell.

[8]  Thomas Lengauer,et al.  A new measure for functional similarity of gene products based on Gene Ontology , 2006, BMC Bioinformatics.

[9]  Weixiong Zhang,et al.  A general co-expression network-based approach to gene expression analysis: comparison and applications , 2010, BMC Systems Biology.

[10]  C. Shin,et al.  The Hot Pepper (Capsicum annuum) MicroRNA Transcriptome Reveals Novel and Conserved Targets: A Foundation for Understanding MicroRNA Functional Roles in Hot Pepper , 2013, PloS one.

[11]  Sungroh Yoon,et al.  Ensemble learning can significantly improve human microRNA target prediction. , 2014, Methods.

[12]  Jing Zhang,et al.  Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[14]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[15]  Mário J. Silva,et al.  Measuring semantic similarity between Gene Ontology terms , 2007, Data Knowl. Eng..

[16]  Xiaochen Bo,et al.  TargetFinder: a software for antisense oligonucleotide target site selection based on MAST and secondary structures of target mRNA. , 2005, Bioinformatics.

[17]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[18]  Qing-Yu He,et al.  A new method for measuring functional similarity of microRNAs , 2011 .

[19]  Rémi Gilleron,et al.  Learning Multi-label Alternating Decision Trees from Texts and Data , 2003, MLDM.

[20]  Razvan C. Bunescu,et al.  Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome , 2005, Genome Biology.

[21]  D. Bartel,et al.  Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. , 2004, Molecular cell.

[22]  Lin Shi,et al.  Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine , 2014, PloS one.

[23]  H. van de Geest,et al.  Identification of microRNA targets in tomato fruit development using high-throughput sequencing and degradome analysis , 2013, Journal of experimental botany.

[24]  Shane T. Jensen,et al.  MicroRNA promoter element discovery in Arabidopsis. , 2006, RNA.

[25]  C. Helliwell,et al.  Regulation of flowering time and floral patterning by miR172. , 2011, Journal of experimental botany.

[26]  Hsien-Da Huang,et al.  miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions , 2013, Nucleic Acids Res..

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

[28]  Marcelo M. Brandão,et al.  AtPIN: Arabidopsis thaliana Protein Interaction Network , 2009, BMC Bioinformatics.

[29]  Jason Weston,et al.  A kernel method for multi-labelled classification , 2001, NIPS.

[30]  Dong Liu,et al.  Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine , 2014, BMC Bioinformatics.

[31]  Wen-Hsiung Li,et al.  Uncovering Small RNA-Mediated Responses to Phosphate Deficiency in Arabidopsis by Deep Sequencing1[W][OA] , 2009, Plant Physiology.

[32]  Haixiu Yang,et al.  Inferring Potential microRNA-microRNA Associations Based on Targeting Propensity and Connectivity in the Context of Protein Interaction Network , 2013, PloS one.

[33]  R. Sunkar,et al.  microRNAs responsive to ozone-induced oxidative stress in Arabidopsis thaliana , 2012, Plant signaling & behavior.

[34]  Matt Geisler,et al.  A predicted protein interactome for rice , 2012, Rice.

[35]  M. Chan,et al.  Sequencing-Based Approaches Reveal Low Ambient Temperature-Responsive and Tissue-Specific MicroRNAs in Phalaenopsis Orchid , 2011, PloS one.

[36]  Ming Chen,et al.  PRIN: a predicted rice interactome network , 2011, BMC Bioinformatics.

[37]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[38]  Michael K. Ng,et al.  Transductive Multilabel Learning via Label Set Propagation , 2013, IEEE Transactions on Knowledge and Data Engineering.

[39]  Sandhya Rani,et al.  Human Protein Reference Database—2009 update , 2008, Nucleic Acids Res..

[40]  Michelangelo Ceci,et al.  Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction , 2013, BMC Bioinformatics.

[41]  Jing Zhang,et al.  Gene selection using rough set based on neighborhood for the analysis of plant stress response , 2014, Appl. Soft Comput..

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

[43]  Jungwon Yoon,et al.  The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community , 2003, Nucleic Acids Res..

[44]  Xi Chen,et al.  Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer , 2009, J. Comput. Biol..

[45]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[46]  Gang Wu,et al.  Temporal regulation of shoot development in Arabidopsis thaliana by miR156 and its target SPL3 , 2006, Development.

[47]  Wei Xiong,et al.  Active learning for protein function prediction in protein-protein interaction networks , 2014, Neurocomputing.

[48]  Matej Oresic,et al.  Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process , 2007, Bioinform..

[49]  A. Krishnamachari,et al.  Computational analysis of plant RNA Pol-II promoters. , 2006, Bio Systems.

[50]  Yang Liu,et al.  Inferring the soybean (Glycine max) microRNA functional network based on target gene network , 2014, Bioinform..