Plant miRNA–lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN

Costa FF (2010) Non-coding RNAs: meet thy masters. BioEssays 32(7):599–608

Heo JB, Lee YS, Sung S (2013) Epigenetic regulation by long noncoding RNAs in plants. Chromosome Res 21(6–7):685–693

Liu YH, Diao HY, Yao YL et al (2016) Long noncoding RNA NEAT1 promotes glioma pathogenesis by regulating miR-449b-5p/c-Met axis. Tumor Biol 37(1):673–683

Ma R, Wang C, Wang J et al (2016) miRNA–mRNA Interaction Network in Non-small Cell Lung Cancer. Interdiscip Sci Comput Life Sci 8(3):209–219

Huang ZA, Huang YA, You ZH et al (2018) Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph. BMC Med Genomics 11(6):113

Paraskevopoulou MD, Hatzigeorgiou AG (2016) Analyzing miRNA-lncRNA interactions. Methods Mol Biol 1402:271–286

Jalali S, Bhartiya D, Lalwani MK et al (2013) Systematic transcriptome wide analysis of lncRNA miRNA interactions. PLoS ONE 8(2):e53823

Thomson DW, Dinger ME (2016) Endogenous microRNA sponges: evidence and controversy. Nat Rev Genet 17(5):272–283

Valiollahi E, Farsi M, Kakhki AM (2014) Sly-miR166 and Sly-miR319 are components of the cold stress response in Solanum lycopersicum. Plant Biotechnol Rep 8(4):349–356

Chen J, Peng H, Han G et al (2018) HOGMMNC: a higher order graph matching with multiple network constraints model for gene–drug regulatory modules identification. Bioinformatics 35(4):602–610

Gu JX, Wang ZH, Kuen J (2018) Recent Advances in Convolutional Neural Networks. Pattern Recogn 77:354–377

Li S, Li W, Cook C, et al (2018) Independently recurrent neural network (IndRNN): building a longer and deeper RNN. In: IEEE conference on computer vision and pattern recognition. https://arxiv.org/abs/1803.04831

Yi X, Zhang Z, Ling Y et al (2015) PNRD: a plant non-coding RNA database. Nucleic Acids Res 43(D1):D982–D989

Andreu PG, Antonio HP, Irantzu Anzar ML et al (2016) GREENC: a Wiki-based database of plant lncRNAs. Nucleic Acids Res 44(D1):D1161–D1166

Dai X, Zhao PX (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 39(suppl):W155–W159

Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48(3):443–453

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

Douzas G, Bacao F (2019) Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE. Inf Sci 501:118–135

Li C, Bovik AC, Wu X (2011) Blind image quality assessment using a general regression neural network. IEEE Trans Neural Networks 22(5):793–799

Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 60(6):84–90

Wang L, Yang J, Liu H et al (2016) Research on a self-adaption algorithm of recurrent neural network based chinese language model. Fire Control Command Control 41(5):31–34

Alipanahi B, Delong A, Weirauch MT et al (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 33(8):831–838

Jin KH, Mccann MT, Froustey E et al (2017) Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process 26(9):4509–4522

Campos Victor, Sastre F, Yagues Maurici et al (2017) Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster. Procedia Comput Sci 108:315–324

Shi H, Xu M, Li R (2018) Deep learning for household load forecasting-a novel pooling deep RNN. IEEE Trans Smart Grid 9(5):5271–5280

Zhou C, You W, Ding X (2010) Genetic algorithm and its implementation of automatic generation of Chinese songci. J Softw 21(3):427–437

Yarotsky D (2017) Error bounds for approximations with deep Relu networks. Neural Netw 94:103–114

An FP (2018) Human action recognition algorithm based on adaptive initialization of deep learning model parameters and support vector machine. IEEE Access 6:59405–59421

Manaswi, Kumar N (2018) Deep learning with applications using Python || understanding and working with Keras. https://link.springer.com/chapter/10.1007/978-1-4842-3516-4_2

Liu T, Yin S (2017) An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation. Multimed Tools Appl 76(9):11961–11974

Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126

Hung H, Jou ZY, Huang SY (2017) Robust mislabel logistic regression without modeling mislabel probabilities. Biometrics 74(1):145–154

Lu X, Wang P, Niyato D (2014) Wireless Networks with RF Energy Harvesting: a Contemporary Survey. IEEE Commun Surv Tutor 17(2):757–789

Xu A, Chen J, Peng H et al (2019) Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences. Front Genet 10:236

Lorenz R, Bernhart SH, Honer Christian, zu Siederdissen CH et al (2011) ViennaRNA package 2.0. Algorithms Mol Biol 6(1):26