M. Abadi, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Q. Abbas, A Review of Computational Methods for Finding Non-Coding RNA Genes, Genes (Basel). 7.12, pp.2073-4425, 2016.

T. Akagi, A Y-chromosome-encoded small RNA acts as a sex determinant in persimmons, Science, vol.346, pp.646-650, 2014.

. Damminda-alahakoon, K. Saman, B. Halgamuge, and . Srinivasan, Dynamic self-organizing maps with controlled growth for knowledge discovery, IEEE Trans. neural networks, vol.11, pp.601-614, 2000.

J. Shea, J. A. Andrews, and . Rothnagel, Emerging evidence for functional peptides encoded by short open reading frames, Nature Reviews Genetics, vol.15, p.193, 2014.

F. Ariel, Battles and hijacks: noncoding transcription in plants, Trends in plant science, vol.20, pp.362-371, 2015.

. Roberto-t-arrial, C. Roberto, M. Togawa, and . Brigido, Screening non-coding RNAs in transcriptomes from neglected species using PORTRAIT: case study of the pathogenic fungus Paracoccidioides brasiliensis, BMC bioinformatics, vol.10, p.239, 2009.

N. Bartonicek, L. Jesper, M. E. Maag, and . Dinger, Long noncoding RNAs in cancer: mechanisms of action and technological advancements

A. Ben-hur, Support vector machines and kernels for computational biology, PLoS computational biology, vol.4, p.1000173, 2008.

Z. Tanya, L. Berardini, and . Reiser, The arabidopsis information resource: Making and mining the "gold standard" annotated reference plant genome, genesis 53, vol.8, pp.1526-968, 2015.

S. Bickel and T. Scheffer, Multi-view clustering, In: ICDM, vol.4, pp.19-26, 2004.

J. Blackmore and R. Miikkulainen, Incremental grid growing: Encoding high-dimensional structure into a two-dimensional feature map, Proceedings of the IEEE International Conference on Neural Networks (ICNN'93), vol.1, pp.450-455, 1993.

A. Blum and T. Mitchell, Combining labeled and unlabeled data with co-training, Proc. Elev. Annu. Conf. Comput. Learn. theory, pp.92-100, 1998.

A. Boucheham, IpiRId: Integrative approach for piRNA prediction using genomic and epigenomic data, PLoS One, vol.12, p.179787, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01630647

R. Boulet, Batch kernel SOM and related Laplacian methods for social network analysis, Neurocomputing 71, vol.7, pp.1257-1273, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00202339

J. Brayet, Towards a piRNA prediction using multiple kernel fusion and support vector machine, Bioinformatics, vol.30, pp.364-370, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01653802

R. Thomas, J. A. Cech, and . Steitz, The noncoding RNA revolution -Trashing old rules to forge new ones, Cell, vol.157, pp.77-94, 2014.

K. Chaudhuri, Multi-view clustering via canonical correlation analysis, Proc. 26th Annu. Int. Conf. Mach. Learn, pp.129-136, 2009.

J. Cheng, piRNA, the new non-coding RNA, is aberrantly expressed in human cancer cells, Clinica chimica acta, vol.412, pp.1621-1625, 2011.

L. Childs, Identification and classification of ncRNA molecules using graph properties, Nucleic Acids Res, vol.37, 2009.

Y. Choe, J. Sirosh, and R. Miikkulainen, Laterally Interconnected Self-Organizing Maps in Hand-Written Digit Recognition, Adv. Neural Inf. Process. Syst, pp.736-742, 1996.

. Seo-won, H. Choi, J. Kim, and . Nam, The small peptide world in long noncoding RNAs, Briefings in Bioinformatics, p.55, 2018.

C. Chow, On optimum recognition error and reject tradeoff, IEEE Trans. Inf. theory, vol.16, pp.41-46, 1970.

, The RNAcentral Consortium, The RNAcentral Consortium, and The RNAcentral Consortium, Nucleic Acids Res

, , pp.305-1048, 2017.

. The-uniprot-consortium, UniProt: the universal protein knowledgebase, Nucleic Acids Res, vol.45, pp.305-1048, 2017.

C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn, vol.20, pp.273-297, 1995.

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. theory, vol.13, pp.21-27, 1967.

C. De-stefano, C. Sansone, and M. Vento, To reject or not to reject: that is the question-an answer in case of neural classifiers, IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev, vol.30, pp.84-94, 2000.

T. Derrien and R. Johnson, The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression, Genome Res, vol.22, pp.1775-1789, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01205054

G. Mikhail and . Dozmorov, Systematic classification of non-coding RNAs by epigenomic similarity, BMC bioinformatics, vol.14, p.2, 2013.

F. Erhard and R. Zimmer, Classification of ncRNAs using position and size information in deep sequencing data, Bioinformatics, vol.26, 2010.

E. Eskin, Mismatch string kernels for SVM protein classification, Advances in neural information processing systems, pp.1441-1448, 2003.

N. Xiao-nan, S. Fan, and . Zhang, lncRNA-MFDL: identification of human long non-coding RNAs by fusing multiple features and using deep learning, Mol. BioSyst, vol.11, issue.3, pp.1742-206, 2015.

. //xlink and . Rsc,

M. Fasold, DARIO: A ncRNA detection and analysis tool for next-generation sequencing experiments, Nucleic Acids Res. 39.SUPPL, vol.2, pp.112-117, 2011.

A. Fiannaca, nRC: non-coding RNA Classifier based on structural features, BioData Min, vol.10, issue.1, p.27, 2017.

W. James and . Fickett, Recognition of protein coding regions in DNA sequences, Nucleic acids research, vol.10, pp.5303-5318, 1982.

L. Fischer, B. Hammer, and H. Wersing, Optimal local rejection for classifiers, Neurocomputing, vol.214, pp.445-457, 2016.

B. Fritzke, A growing neural gas network learns topologies, Advances in neural information processing systems, pp.625-632, 1995.

B. Fritzke, Growing grid-a self-organizing network with constant neighborhood range and adaptation strength, pp.9-13

G. Fumera, F. Roli, and G. Giacinto, Reject option with multiple thresholds, Pattern Recognit. 33, vol.12, pp.2099-2101, 2000.

F. Galton, Regression towards mediocrity in hereditary stature, In: J. Anthropol. Inst. Gt. Britain Irel, vol.15, pp.246-263, 1886.

O. Gevaert, Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks, Bioinformatics, vol.22, 2006.

F. Girosi, M. Jones, and T. Poggio, Regularization Theory and Neural Networks Architectures, Neural Comput, vol.7, pp.219-269, 1995.

M. Gönen and E. Alpayd?n, Localized algorithms for multiple kernel learning, Pattern Recognition, vol.46, pp.795-807, 2013.

M. Gönen and E. Alpayd?n, Multiple kernel learning algorithms, Journal of machine learning research, vol.12, pp.2211-2268, 2011.

M. Gönen and A. Ghahramani, Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology, Adv. Neural Inf. Process. Syst. 27, pp.1305-1313, 2014.

W. John and . Graham, Missing data analysis: Making it work in the real world, Annual review of psychology, vol.60, pp.549-576, 2009.

X. Guo, Advances in long noncoding RNAs: Identification, structure prediction and function annotation, Brief. Funct. Genomics, vol.15, p.20412657, 2016.

J. David, K. Hand, and . Yu, Idiot's Bayes-not so stupid after all?, In: Int. Stat. Rev, vol.69, pp.385-398, 2001.

S. Haykin, Neural Networks: A Comprehensive Foundation, pp.236-284, 1994.

T. Hecht, M. Lefort, and A. Gepperth, Using self-organizing maps for regression: the importance of the output function, European Symposium on Artificial Neural Networks (ESANN), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01251011

K. Tin and . Ho, Random decision forests, Doc. Anal. Recognition, 1995., Proc. Third Int. Conf, vol.1, pp.278-282, 1995.

G. Housman and I. Ulitsky, Methods for distinguishing between protein-coding and long noncoding RNAs and the elusive biological purpose of translation of long noncoding RNAs, Biochim. Biophys. Acta -Gene Regul. Mech. 1859, vol.1, pp.31-40, 2016.

L. Hu, COME: a robust coding potential calculation tool for lncRNA identification and characterization based on multiple features, Nucleic Acids Res, vol.45, issue.1, 2017.

T. Nicholas and . Ingolia, Ribosome profiling: new views of translation, from single codons to genome scale, Nature Reviews Genetics, vol.15, p.205, 2014.

H. Ishibuchi and M. Nii, Neural networks for soft decision making, Fuzzy Sets Syst, vol.115, pp.121-140, 2000.

E. A. Ito, BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification, Nucleic Acids Research, 2018.

S. Jalali, Computational approaches towards understanding human long non-coding RNA biology, Bioinformatics, vol.31, p.14602059, 2015.

D. Skok, Genome-wide in silico screening for micro RNA genetic variability in livestock species, Animal genetics, vol.44, pp.669-677, 2013.

I. Kalvari, Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families, Nucleic Acids Res, pp.305-1048, 2017.

M. Kan, Multi-view discriminant analysis, IEEE transactions on pattern analysis and machine intelligence, vol.38, pp.188-194, 2016.

Y. Kang and D. Yang, CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features, Nucleic Acids Res, vol.45, pp.12-16, 2017.

K. Kashi, Discovery and functional analysis of lncRNAs: methodologies to investigate an uncharacterized transcriptome, Biochimica et Biophysica Acta (BBA)-Gene Regulatory Mechanisms 1859, vol.1, pp.3-15, 2016.

P. Diederik, J. Kingma, and . Ba, Adam: A Method for Stochastic Optimization, 2014.

S. Kittiwachana and K. Grudpan, Supervised Self Organizing Maps for explonatory data analysis of running waters on physiochemical parameters: a case study in Chiang Mai, Thailand". In: KKU Res.j, vol.20, pp.1-11, 2015.

T. Kiuchi, A single female-specific piRNA is the primary determiner of sex in the silkworm, Nature, vol.509, pp.633-636, 2014.

T. Kohonen, Self-Organizing Maps -third edition, 2001.

T. Kohonen, The'neural'phonetic typewriter, Computer (Long. Beach. Calif ), vol.21, issue.3, pp.11-22, 1988.

L. Kong, CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine, Nucleic Acids Res, vol.35, 2007.

R. G. Gert and . Lanckriet, A statistical framework for genomic data fusion, Bioinformatics, vol.20, pp.2626-2635, 2004.

H. King-wai-lau, S. Yin, and . Hubbard, Kernel self-organising maps for classification, Neurocomputing 69, vol.16, pp.2033-2040, 2006.

Y. Lecun, C. Cortes, and C. J. Burges, THE MNIST DATABASE of handwritten digits

B. Lee, deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks, Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

A. Legendre, E. Angel, and F. Tahi, Bi-objective integer programming for RNA secondary structure prediction with pseudoknots, BMC bioinformatics, vol.19, p.13, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01696144

Y. Lei, Localized Multiple Kernel Learning -A Convex Approach, 2015.

Y. Y. Leung, CoRAL: Predicting non-coding RNAs from small RNA-sequencing data, Nucleic Acids Res. 41, vol.14, 2013.

A. Li, J. Zhang, and Z. Zhou, PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme, BMC Bioinformatics, vol.15, pp.1471-2105, 2014.

M. Li, Multiple Kernel Clustering with Local Kernel Alignment Maximization, Proc. Twenty-Fifth Int. Jt. Conf. Artif. Intell. IJCAI, pp.9-15, 2016.

Y. Li and H. Xiong, Union of data-driven subspaces via subspace clustering for compressive video sampling, Data Compression Conf. Proc, p.10680314, 2014.

M. Liang, Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), vol.12, pp.928-937, 2015.

M. Lichman, UCI Machine Learning Repository, 2013.

J. Liu, J. Gough, and B. Rost, Distinguishing Protein-Coding from Non-Coding RNAs through Support Vector Machines, PLoS Genet, vol.2, issue.4, 2006.

R. Lorenz, ViennaRNA Package 2.0, Algorithms for Molecular Biology, vol.6, p.26, 2011.

J. Lu, MicroRNA expression profiles classify human cancers". In: nature 435, vol.7043, p.834, 2005.

L. Ma, V. B. Bajic, and Z. Zhang, On the classification of long non-coding RNAs, RNA Biology, vol.10, issue.6, pp.924-933, 2013.

J. Mariette, Bagged kernel SOM". In: Adv. Self-Organizing Maps Learn. Vector Quantization, pp.45-54, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01018374

L. César, G. Mattos, and . Barreto, ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks, Neural Comput. Appl, vol.22, issue.1, pp.49-61, 2013.

Y. Mu and B. Zhou, Non-uniform Multiple Kernel Learning with Cluster-based Gating Functions, pp.925-2312, 2011.

E. P. Nawrocki, Rfam 12.0: updates to the RNA families database, Nucleic Acids Research, vol.43, pp.130-137, 2015.

A. Nuala and . Leary, Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation, Nucleic Acids Res, vol.44, pp.733-778, 2016.

M. Olteanu, N. Villa-vialaneix, and C. Cierco-ayrolles, Multiple Kernel Self-Organizing Maps, Eur. Symp. Artif. Neural Networks, p.83, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00817920

C. Ken, . Pang, C. Martin, J. S. Frith, and . Mattick, Rapid evolution of noncoding RNAs: lack of conservation does not mean lack of function, Trends in genetics, vol.22, issue.1, pp.1-5, 2006.

B. Panwar, A. Arora, and G. Raghava, Prediction and classification of ncRNAs using structural information, BMC Genomics, vol.15, pp.1471-2164, 2014.

F. Payre and C. Desplan, RNA. Small peptides control heart activity, In: Science, vol.351, pp.1095-9203, 2016.
URL : https://hal.archives-ouvertes.fr/hal-00886201

F. Pedregosa, Scikit-learn: Machine Learning in Python", In: J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

K. Pelckmans and . De-moor, Mutual Spectral clustering: Microarray Experiments Versus Text Corpus, Work. Probabilistic Model. Mach. Learn. Struct. Syst. Biol, pp.55-58, 2006.

M. Diane and . Pereira, Delivering the promise of miRNA cancer therapeutics, Drug discovery today, vol.18, pp.282-289, 2013.

S. Anton and . Petrov, Secondary structures of rRNAs from all three domains of life, PLoS One, vol.9, p.88222, 2014.

D. Therese and . Pigott, A review of methods for missing data, pp.353-383, 2001.

L. Platon, F. Zehraoui, and F. Tahi, Localized multiple sources Self-Organizing Map, 25th International Conference on Neural Information Processing: ICONIP 2018, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01971022

L. Platon, F. Zehraoui, and F. Tahi, Self-organizing maps with supervised layer, 2017 12th Int. Work. Self-Organizing Maps Learn. Vector Quantization, Clust. Data Vis, pp.1-8, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01629610

L. Platon, F. Zehraoui, and F. Tahi, Self-organizing maps with supervised layer, Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM, pp.1-8, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01629610

L. Platon, IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection, Bioinformatics, vol.34, pp.620-628, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01810612

Y. Prudent and A. Ennaji, An incremental growing neural gas learns topologies, Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on, vol.2, pp.1211-1216, 2005.

C. A. Raphael-heiko-rastetter, D. Smith, and . Wilhelm, The role of non-coding RNA in male sex determination and differentiation, Reproduction, p.15, 2015.

M. Re and G. Valentini, Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines, Neurocomputing 73, vol.7, pp.1533-1537, 2010.

K. Sato, IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming, Bioinformatics, vol.27, pp.85-93, 2011.

L. Joseph, J. Schafer, and . Graham, Missing data: our view of the state of the art, Psychological methods, vol.7, issue.2, p.147, 2002.

U. Singh, PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea, Nucleic Acids Research, vol.45, p.183, 2017.

R. Sousa, Robust classification with reject option using the self-organizing map, Neural Comput. Appl, vol.26, pp.1603-1619, 2015.

N. Srivastava, R. Ruslan, and . Salakhutdinov, Multimodal learning with deep boltzmann machines, Advances in neural information processing systems, pp.2222-2230, 2012.

G. St, C. Laurent, P. Wahlestedt, and . Kapranov, The Landscape of long noncoding RNA classification, Trends Genet, vol.31, p.13624555, 2015.

K. Sun, iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data, S7. issn, vol.14, pp.1471-2164, 2013.

L. Sun, lncRScan-SVM: a tool for predicting long non-coding RNAs using support vector machine, PloS one, vol.10, p.139654, 2015.

L. Sun, Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts, Nucleic Acids Res, vol.41, 2013.

F. Tahi and A. Boucheham, In Silico Prediction of RNA Secondary Structure, Promoter Associated RNA, pp.145-168, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01630646

C. Tav, miRNAFold: a web server for fast miRNA precursor prediction in genomes, Nucleic Acids Research, vol.44, pp.181-184, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01440772

S. Tempel, miRBoost: boosting support vector machines for microRNA precursor classification, RNA, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01146718

S. Tempel and F. Tahi, A fast ab-initio method for predicting miRNA precursors in genomes, Nucleic Acids Res, vol.40, pp.80-80, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00667075

R. Tripathi, Deeplnc, a long non-coding rna prediction tool using deep neural network, Network Modeling Analysis in Health Informatics and Bioinformatics, vol.5, p.21, 2016.

S. P. Jwc-van-lint and . Hoogendoorn, A robust and efficient method for fusing heterogeneous data from traffic sensors on freeways, Computer-Aided Civil and Infrastructure Engineering, vol.25, pp.596-612, 2010.

P. Videm, BlockClust: Efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles, Bioinformatics, vol.30, p.14602059, 2014.

T. Voegtlin, Recursive self-organizing maps, Neural Networks, vol.15, pp.979-991, 2002.

W. Wan and D. Fraser, A multiple self-organizing map scheme for remote sensing classification, Int. Work. Mult. Classif. Syst. Springer, pp.300-309, 2000.

C. Wang, Computational Approaches in Detecting Non-Coding RNA, Curr. Genomics, vol.14, pp.1389-2029, 2013.

J. Wang, J. Zhuang, and S. Hoi, Unsupervised Multiple Kernel Learning, J. Mach. Learn. Res. -Proc. Track, vol.20, pp.129-144, 2011.

L. Wang, CPAT: Coding-potential assessment tool using an alignment-free logistic regression model, Nucleic Acids Res. 41, vol.6, p.3051048, 2013.

Q. Wang, Local kernel alignment based multi-view clustering using extreme learning machine, Neurocomputing, vol.275, pp.1099-1111, 2018.

S. Washietl, RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data, Rna, 2011.

S. Xiang, Bi-level multi-source learning for heterogeneous block-wise missing data, NeuroImage, vol.102, pp.192-206, 2014.

C. Xu, D. Tao, and C. Xu, A Survey on Multi-view Learning, 2013.

J. Yang, Group-Sensitive Multiple Kernel Learning for Object Categorization, ICCV, 2009.

J. Ye, Heterogeneous data fusion for alzheimer's disease study, Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

L. Yuan, Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data, NeuroImage, vol.61, pp.622-632, 2012.

P. Daniel-r-zerbino and . Achuthan, Ensembl, Nucleic Acids Res, vol.46, pp.754-761, 2018.

Y. Zhang, A review on recent computational methods for predicting noncoding RNAs, BioMed research international 2017, 2017.

B. Zhao, T. James, C. Kwok, and . Zhang, Multiple Kernel Clustering, SDM, pp.638-649, 2009.

X. Zhao, N. Evans, and J. Dugelay, A subspace co-training framework for multi-view clustering, Pattern Recognition Letters, 2013.

D. Zhou and C. Burges, Spectral clustering and transductive learning with multiple views, ICML '07 Proc. 24th Int. Conf. Mach. Learn, pp.1159-1166, 2007.

M. ?itnik and B. Zupan, Data Fusion by Matrix Factorization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, pp.162-8828, 2015.