Herbst RS, Heymach JV, Lippman SM. Lung cancer. New England J Med. 2008; 359(13):1367–80. https://doi.org/10.1056/NEJMra0802714. PMID: 18815398.
Article
CAS
Google Scholar
Ansorge WJ. Next-generation dna sequencing techniques. New Biotechnol. 2009; 25(4):195–203.
Article
CAS
Google Scholar
Werner T. Next generation sequencing in functional genomics. Brief Bioinformatics. 2010; 11(5):499–511.
Article
CAS
PubMed
Google Scholar
Chen R, Snyder M. Promise of personalized omics to precision medicine. Wiley Interdiscipl Rev: Syst Biol Med. 2013; 5(1):73–82.
Google Scholar
Seo D, Ginsburg GS. Genomic medicine: bringing biomarkers to clinical medicine. Curr Opin Chem Biol. 2005; 9(4):381–6.
Article
CAS
PubMed
Google Scholar
Emmert-Streib F, Tuomisto L, Yli-Harja O. The Need for Formally Defining ’Modern Medicine’ by Means of Experimental Design. Frontiers Genet. 2016; 7:60. https://doi.org/10.3389/fgene.2016.00060.
Google Scholar
Anastasiadou E, Jacob LS, Slack FJ. Non-coding rna networks in cancer. Nature Rev Cancer. 2018; 18(1):5.
Article
CAS
Google Scholar
Cech TR, Steitz JA. The noncoding rna revolution?trashing old rules to forge new ones. Cell. 2014; 157(1):77–94.
Article
CAS
PubMed
Google Scholar
Fatica A, Bozzoni I. Long non-coding rnas: new players in cell differentiation and development. Nature Rev Genet. 2014; 15(1):7.
Article
CAS
PubMed
Google Scholar
Mercer TR, Dinger ME, Mattick JS. Long non-coding rnas: insights into functions. Nature Rev Genet. 2009; 10(3):155.
Article
CAS
PubMed
Google Scholar
QD Wang X, L Crutchley J, Dostie J. Shaping the genome with non-coding rnas. Curr Genomics. 2011; 12(5):307–21.
Article
Google Scholar
Sacco LD, Baldassarre A, Masotti A. Bioinformatics tools and novel challenges in long non-coding rnas (lncrnas) functional analysis. Int J Mole Sci. 2011; 13(1):97–114.
Article
CAS
Google Scholar
Ponting CP, Belgard TG. Transcribed dark matter: meaning or myth?Human Mole Genet. 2010; 19(R2):162–8.
Article
CAS
Google Scholar
Robinson R. Dark matter transcripts: sound and fury, signifying nothing?PLoS Biol. 2010; 8(5):1000370.
Article
CAS
Google Scholar
Managadze D, Rogozin IB, Chernikova D, Shabalina SA, Koonin EV. Negative correlation between expression level and evolutionary rate of long intergenic noncoding rnas. Genome Biol Evol. 2011; 3:1390–1404.
Article
CAS
PubMed
PubMed Central
Google Scholar
Amaral PP, Clark MB, Gascoigne DK, Dinger ME, Mattick JS. lncrnadb: a reference database for long noncoding rnas. Nucleic Acids Res. 2010; 39(suppl_1):146–151.
Article
CAS
Google Scholar
Moran VA, Perera RJ, Khalil AM. Emerging functional and mechanistic paradigms of mammalian long non-coding rnas. Nucleic Acids Res. 2012; 40(14):6391–400.
Article
CAS
PubMed
PubMed Central
Google Scholar
Carninci P, Kasukawa T, Katayama S, Gough J, Frith M, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, et al. The transcriptional landscape of the mammalian genome. Science. 2005; 309(5740):1559–63.
Article
CAS
PubMed
Google Scholar
Kapranov P, Cheng J, Dike S, Nix DA, Duttagupta R, Willingham AT, Stadler PF, Hertel J, Hackermüller J, Hofacker IL, et al. Rna maps reveal new rna classes and a possible function for pervasive transcription. Science. 2007; 316(5830):1484–8.
Article
CAS
PubMed
Google Scholar
Esteller M. Non-coding rnas in human disease. Nature Rev Genet. 2011; 12(12):861.
Article
CAS
PubMed
Google Scholar
Palazzo AF, Lee ES. Non-coding rna: what is functional and what is junk?Front Genet. 2015; 6:2.
Article
PubMed
PubMed Central
CAS
Google Scholar
Mattick JS. The genetic signatures of noncoding rnas. PLoS Genet. 2009; 5(4):1000459.
Article
CAS
Google Scholar
Glazko GV, Zybailov BL, Rogozin IB. Computational prediction of polycomb-associated long non-coding rnas. PLoS ONE. 2012; 7(9):44878.
Article
CAS
Google Scholar
Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, et al.Unique microrna molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006; 9(3):189–98.
Article
CAS
PubMed
Google Scholar
Su X, Malouf GG, Chen Y, Zhang J, Yao H, Valero V, Weinstein JN, Spano J-P, Meric-Bernstam F, Khayat D, et al. Comprehensive analysis of long non-coding rnas in human breast cancer clinical subtypes. Oncotarget. 2014; 5(20):9864.
Article
PubMed
PubMed Central
Google Scholar
Li R, Qian J, Wang Y-Y, Zhang J-X, You Y-P. Long noncoding rna profiles reveal three molecular subtypes in glioma. CNS Neurosci Therapeu. 2014; 20(4):339–43.
Article
CAS
Google Scholar
Flippot R, Malouf GG, Su X, Mouawad R, Spano J-P, Khayat D. Cancer subtypes classification using long non-coding rna. Oncotarget. 2016; 7(33):54082.
Article
PubMed
PubMed Central
Google Scholar
Seo J-S, Ju YS, Lee W-C, Shin J-Y, Lee JK, Bleazard T, Lee J, Jung YJ, Kim J-O, Shin J-Y, et al.The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res. 2012; 22:2109–19.
Article
CAS
PubMed
PubMed Central
Google Scholar
Cestarelli V, Fiscon G, Felici G, Bertolazzi P, Weitschek E. Camur: Knowledge extraction from rna-seq cancer data through equivalent classification rules. Bioinformatics. 2015; 32(5):697–704.
Article
PubMed
PubMed Central
CAS
Google Scholar
Guo Y, Liu S, Li Z, Shang X. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinformatics. 2018; 19(5):118.
Article
PubMed
PubMed Central
Google Scholar
Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006; 18(7):1527–54.
Article
PubMed
Google Scholar
Breiman L. Random Forests. Mach Learn. 2001; 45:5–32.
Article
Google Scholar
Chang C-C, Lin C-J. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol. 2011; 2:27–12727. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Article
Google Scholar
Weitschek E, Di Lauro S, Cappelli E, Bertolazzi P, Felici G. Camurweb: a classification software and a large knowledge base for gene expression data of cancer. BMC Bioinformatics. 2018; 19(10):245.
Google Scholar
Minsky M, Papert S. Perceptrons. Cambridge: MIT Press; 1969.
Google Scholar
Crick F. The recent excitement about neural networks. Nature. 1989; 337:129–32.
Article
CAS
PubMed
Google Scholar
Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA. 1982; 79:2554–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Emmert-Streib F. Active learning in recurrent neural networks facilitated by an hebb-like learning rule with memory. Neural Inf Process - Lett Rev. 2005; 9(2):31–40.
Google Scholar
Emmert-Streib F. A heterosynaptic learning rule for neural networks. Int J Modern Phys C. 2006; 17(10):1501–20.
Article
Google Scholar
Rosenblatt F. The Perceptron, a Perceiving and Recognizing Automaton Project Para: Cornell Aeronautical Laboratory; 1957.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436–44.
Article
CAS
PubMed
Google Scholar
Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks: Curran Associates, Inc; 2012, pp. 1097–1105. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.
Graves A, Mohamed A, Hinton GE. Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2013; abs/1303.5778. https://doi.org/10.1109/icassp.2013.6638947.
Leung MKK, Xiong HY, Lee LJ, Frey BJ. Deep learning of the tissue-regulated splicing code. Bioinformatics. 2014; 30(12):121–9.
Article
CAS
Google Scholar
Zhang S, Zhou J, Hu H, Gong H, Chen L, Cheng C, Zeng J. A deep learning framework for modeling structural features of rna-binding protein targets. Nucleic Acids Res. 2015; 43(20):e32.
Google Scholar
Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nat Biotechnol. 2015; 33:831–8.
Article
CAS
PubMed
Google Scholar
Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the International Conference on Machine Learning, vol. 28: 2013.
Stupnikov A, Tripathi S, de Matos Simoes R, McArt D, Salto-Tellez M, Glazko G, Emmert-Streib F. samExploreR: Exploring reproducibility and robustness of RNA-seq results based on SAM files. Bioinformatics. 2016; 32:475.
Article
CAS
Google Scholar
Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2010; 39:19–21.
Article
CAS
Google Scholar
Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nature Methods. 2012; 9(4):357–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Karolchik D, Barber GP, Casper J, Clawson H, Cline MS, Diekhans M, Dreszer TR, Fujita PA, Guruvadoo L, Haeussler M, et al.The ucsc genome browser database: 2014 update. Nucleic Acids Res. 2014; 42(D1):764–770.
Liao Y, Smyth GK, Shi W. featurecounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2013:656. https://academic.oup.com/bioinformatics/article/30/7/923/232889.
Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, et al. A survey of best practices for rna-seq data analysis. Genome Biol. 2016; 17(1):13.
Quek XC, Thomson DW, Maag JL, Bartonicek N, Signal B, Clark MB, Gloss BS, Dinger ME. lncrnadb v2. 0: expanding the reference database for functional long noncoding rnas. Nucleic Acids Res. 2014; 43(D1):168–73.
Article
CAS
Google Scholar
Emmert-Streib F, Moutari S, Dehmer M. A comprehensive survey of error measures for evaluating binary decision making in data science. Wiley Interdiscipl Rev: Data Mining Knowl Disc. 2019:1303. https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1303.
Webb AR, Copsey KD. Statistical Pattern Recognition, 3rd. Rochelle Park: Wiley; 2011.
Book
Google Scholar
Bradley AP. The use of the area under the roc curve in the evaluation of machine learning algorithms. Patt Recogn. 1997; 30(7):1145–59.
Article
Google Scholar
Japkowicz N, Stephen S. The class imbalance problem: A systematic study. Intell Data Anal. 2002; 6(5):429–49.
Article
Google Scholar
Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005; 21(15):3301–07.
Article
CAS
PubMed
Google Scholar
Emmert-Streib F, Dehmer M. Evaluation of regression models: Model assessment, model selection and generalization error. Mach Learn Knowl Extract. 2019; 1(1):521–51.
Article
Google Scholar
Yoshua B. Learning deep architectures for ai. Foundations Trends Mach Learn. 2009; 2(1):1–127. https://doi.org/10.1561/2200000006.
Article
Google Scholar
Fischer A, Igel C. An introduction to restricted boltzmann machines. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer: 2012. p. 14–36. http://image.diku.dk/igel/paper/AItRBM-proof.pdf.
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006; 313(5786):504–7.
Article
CAS
PubMed
Google Scholar
Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: Neural Networks, 1993., IEEE International Conference On. IEEE: 1993. p. 586–91. https://doi.org/10.1109/icnn.1993.298623.
Igel C, Hüsken M. Improving the rprop learning algorithm. In: Proceedings of the Second International ICSC Symposium on Neural Computation (NC 2000), vol. 2000. Citeseer: 2000. p. 115–21.
Drees M. Darch: Package for Deep Architectures and Restricted-Bolzmann-Machines. The Comprehensive R Archive Network (CRAN). 2014. The Comprehensive R Archive Network (CRAN). Version 0.9.1. https://cran.fhcrc.org/web/packages/darch/index.html.
Salakhutdinov R, Hinton GE. Deep boltzmann machines. In: International Conference on Artificial Intelligence and Statistics: 2009. p. 448–55.
Hinton G. Where do features come from?Cognitive Sci. 2014; 38(6):1078–101.
Article
Google Scholar
Zhao J, Cheng W, He X, Liu Y, Li J, Sun J, Li J, Wang F, Gao Y. Construction of a specific SVM classifier and identification of molecular markers for lung adenocarcinoma based on lncrna-mirna-mRNA network. OncoTargets Therapy. 2018; 11:3129.
Article
PubMed
PubMed Central
Google Scholar
Fan Z, Xue W, Li L, Zhang C, Lu J, Zhai Y, Suo Z, Zhao J. Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model. J Trans Med. 2018; 16(1):205.
Article
CAS
Google Scholar
Pirooznia M, Yang JY, Yang MQ, Deng Y. A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics. 2008; 9(1):13.
Article
Google Scholar
Salem H, Attiya G, El-Fishawy N. Gene expression profiles based human cancer diseases classification. In: Computer Engineering Conference (ICENCO), 2015 11th International. IEEE: 2015. p. 181–7. https://doi.org/10.1109/icenco.2015.7416345.
Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D. Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics. 2005; 21(20):3896–904.
Article
CAS
PubMed
Google Scholar
Wei X, Li K-C. Exploring the within-and between-class correlation distributions for tumor classification. Proc Nat Acad Sci. 2010; 107(15):6737–42.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wang X. Robust two-gene classifiers for cancer prediction. Genomics. 2012; 99(2):90–5.
Article
CAS
PubMed
Google Scholar
Liu J, Wang X, Cheng Y, Zhang L. Tumor gene expression data classification via sample expansion-based deep learning. Oncotarget. 2017; 8(65):109646.
PubMed
PubMed Central
Google Scholar
Roffo G, Melzi S, Cristani M. Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision: 2015. p. 4202–10.
Xue Z, Wen J, Chu X, Xue X. A microrna gene signature for identification of lung cancer. Surg Oncol. 2014; 23(3):126–31.
Article
PubMed
Google Scholar
Volinia S, Calin GA, Liu C-G, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, et al. A microrna expression signature of human solid tumors defines cancer gene targets. Proc Nat Acad Sci. 2006; 103(7):2257–61.
Article
CAS
Google Scholar
Telonis AG, Magee R, Loher P, Chervoneva I, Londin E, Rigoutsos I. Knowledge about the presence or absence of mirna isoforms (isomirs) can successfully discriminate amongst 32 tcga cancer types. Nucleic Acids Res. 2017; 45(6):2973–85.
Article
CAS
PubMed
PubMed Central
Google Scholar
Seow N, Fenati RA, Connolly AR, Ellis AV. Hi-fidelity discrimination of isomiRs using G-quadruplex gatekeepers. PloS one. 2017; 12(11):0188163.
Article
CAS
Google Scholar
Brown G, Pocock A, Zhao M-J, Luján M. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res. 2012; 13(Jan):27–66.
Google Scholar
Dash M, Liu H. Feature selection for classification. Intell Data Anal. 1997; 1(3):131–56.
Article
Google Scholar
Yang HH, Moody J. Data visualization and feature selection: New algorithms for nongaussian data. In: Advances in Neural Information Processing Systems: 2000. p. 687–93.
Waddington CH. The Strategy of the Genes. New York: Geo, Allen Unwin, London; 1957.
Google Scholar
Kauffman SA. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theoret Biol. 1969; 22:437–67.
Article
CAS
Google Scholar
Becskei A, Séraphin B, Serrano L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 2001; 20(10):2528–35.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chen Y-R, Huang H-C, Lin C-C. Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis. Brief Bioinforma. 2017.
Herranz H, Cohen SM. Micrornas and gene regulatory networks: managing the impact of noise in biological systems. Genes Dev. 2010; 24(13):1339–44.
Article
CAS
PubMed
PubMed Central
Google Scholar
Telonis AG, Loher P, Jing Y, Londin E, Rigoutsos I. Beyond the one-locus-one-mirna paradigm: microrna isoforms enable deeper insights into breast cancer heterogeneity. Nucleic Acids Res. 2015; 43(19):9158–75.
Article
CAS
PubMed Central
PubMed
Google Scholar
Anastasiadou E, Faggioni A, Trivedi P, Slack FJ. The nefarious nexus of noncoding rnas in cancer. Int J Mole Sci. 2018; 19(7). https://doi.org/10.20944/preprints201803.0187.v1.
Yamamura S, Imai-Sumida M, Tanaka Y, Dahiya R. Interaction and cross-talk between non-coding rnas. Cell Mole Life Sci. 2017:1–18. https://link.springer.com/article/10.1007/s00018-017-2626-6.
Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011; 7(10):1002240.
Article
CAS
Google Scholar