DASSI: tìm kiếm kiến trúc vi sai cho việc nhận diện splice từ chuỗi DNA
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#Genomics #Deep Learning #Splice Site Recognition #DNA Sequences #Architecture Search #Neural NetworksTài liệu tham khảo
Baldi P, Sadowski P, Whiteson D. Searching for exotic particles in high-energy physics with deep learning. Nat Commun. 2014; 5:4308.
Goh G, Hodas N, Vishnu A. Deep learning for computational chemistry. J Comput Chem. 2017; 38(16):1291–307.
Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nat. 2017; 542(7639):115.
Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K. Hierarchical representations for efficient architecture search. 2017. Preprint at https://arxiv.org/abs/1711.00436.
Real E, Aggarwal A, Huang Y, Le Q. Regularized evolution for image classifier architecture search. Proceedings of the Thirty-Third AAAI conference on artificial intelligence. 2019; 33(1):4780–4789.
Zoph B, Le QV. Neural architecture search with reinforcement learning. 2016. Preprint at https://arxiv.org/abs/1611.01578.
Zoph B, Vasudevan V, Shlens J, Le Q. Learning transferable architectures for scalable image recognition. In: Proceedings of the Thirty-First IEEE conference on computer vision and pattern recognition: 18-22 June 2018. Utah: 2017. p. 8697–8710.
Summers P. A methodology for lisp program construction from examples. J ACM (JACM). 1977; 24(1):161–75.
Baker B, Gupta O, Raskar R, Naik N. Accelerating neural architecture search using performance prediction. 2017. Preprint at https://arxiv.org/abs/1705.10823.
Brock A, Lim T, Ritchie JM, Weston N. Smash: one-shot model architecture search through hypernetworks. 2017. Preprint at https://arxiv.org/abs/1708.05344.
Pham H, Guan M, Zoph B, Le Q, Dean J. Efficient neural architecture search via parameters sharing. In: Proceedings of the Thirty-Fifth International Conference on Machine Learning: 10-15 July. Stockholm: 2018. p. 4095–4104.
Liu H, Simonyan K, Yang Y. Darts: Differentiable architecture search. 2018. Preprint at https://arxiv.org/abs/1806.09055.
Lee B, Lee T, Na B, Yoon S. DNA-level splice junction prediction using deep recurrent neural networks. 2015. Preprint at https://arxiv.org/abs/1512.05135.
Au K, Jiang H, Lin L, Xing Y, Wong W. Detection of splice junctions from paired-end rna-seq data by splicemap. Nucleic Acids Res. 2010; 38(14):4570–8.
Trapnell C, Pachter L, Salzberg S. Tophat: discovering splice junctions with rna-seq. Bioinforma. 2009; 25(9):1105–11.
Baten AK, Chang BC, Halgamuge SK, Li J. Splice site identification using probabilistic parameters and svm classification. BMC Bioinformatics BioMed Central. 2006; 7(5):1–15.
Meher P, Sahu T, Rao A, Wahi S. Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithm Mol Biol. 2016; 11(1):16.
Zhang Y, Chu C-H, Chen Y, Zha H, Ji X. Splice site prediction using support vector machines with a bayes kernel. Expert Syst Appl. 2006; 30(1):73–81.
Wei D, Zhuang W, Jiang Q, Wei Y. A new classification method for human gene splice site prediction In: He J, Liu X, Krupinski EA, Xu G, editors. Health Information Science. Springer: 2012. p. 121–30.
Pashaei E, Aydin N. Markovian encoding models in human splice site recognition using svm. Comput Biol Chem. 2018; 73:159–70.
Pashaei E, Yilmaz A, Aydin N. A combined SVM and Markov model approach for splice site identification. In: Proceedings of the Sixth International Conference on Computer and Knowledge Engineering (ICCKE): 20-21 October 2016. Mashhad: IEEE: 2016. p. 200–4.
Meher P, Sahu T, Rao A. Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData Min. 2016; 9(1):4.
Pashaei E, Ozen M, Aydin N. Splice site identification in human genome using random forest. Health Technol. 2017; 7(1):141–52.
Pashaei E, Ozen M, Aydin N. Random forest in splice site prediction of human genome. In: Proceedings of the Fourteenth Mediterranean Conference on Medical and Biological Engineering and Computing: 31 March-2 April 2016. Paphos: Springer: 2016. p. 518–23.
Lopes H, Erig Lima C, Murata N. A configware approach for high-speed parallel analysis of genomic data. J Circ Syst Comput. 2007; 16(04):527–40.
Kamath U, De Jong K, Shehu A. Effective automated feature construction and selection for classification of biological sequences. PloS one. 2014; 9(7):99982.
Zhang Q, Peng Q, Zhang Q, Yan Y, Li K, Li J. Splice sites prediction of human genome using length-variable markov model and feature selection. Expert Syst Appl. 2010; 37(4):2771–82.
Pashaei E, Yilmaz A, Ozen M, Aydin N. Prediction of splice site using AdaBoost with a new sequence encoding approach. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC): 9-12 October 2016. Budapest: IEEE: 2016. p. 3853–3858.
Pashaei E, Yilmaz A, Ozen M, Aydin N. A novel method for splice sites prediction using sequence component and hidden markov model. In: Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC):16-20 August 2016. Florida: IEEE: 2016. p. 3076–9.
Pashaei E, Ozen M, Aydin N. Splice sites prediction of human genome using AdaBoost. In: Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI):24-27 February 2016. Las Vegas: IEEE: 2016. p. 300–3.
Pashaei E, Aydin N. Frequency difference based DNA encoding methods in human splice site recognition. In: Proceedings of the International Conference on Computer Science and Engineering (UBMK):5-7 July 2017. London: IEEE: 2017. p. 586–91.
Ryen T, Eftes T, Kjosmoen T, Ruoff P, et al. Splice site prediction using artificial neural networks. In: Proceedings of the Fifth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics:3-4 October 2008. Berlin: Springer: 2008. p. 102–13.
Elsousy R, Kathiresan N, Boughorbel S. On the depth of deep learning models for splice site identification. bioRxiv,. 2018:380667.
Du X, Yao Y, Diao Y, Zhu H, Zhang Y, Li S. Deepss: Exploring splice site motif through convolutional neural network directly from dna sequence. IEEE Access. 2018; 6:32958–78.
Albaradei S, Magana-Mora A, Thafar M, Uludag M, Bajic VB, Gojobori T, Magbubah E, Jankovic BR. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA. Gene: X. 2020; 5:100035.
Wang R, Wang Z, Wang J, Li S. Splicefinder: ab initio prediction of splice sites using convolutional neural network. BMC Bioinforma. 2019; 20(23):652.
Kothen-Hill ST, Zviran A, Schulman RC, Deochand S, Gaiti F, Maloney D, Huang K, Liao W, Robine N, Omans ND, Landau D. Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy. In: Proceedings of the Sixth International Conference on Learning Representations: 30 April-3 May 2018. Vancouver: 2018.
Lee T, Yoon S. Boosted categorical restricted Boltzmann machine for computational prediction of splice junctions. In: Proceedings of the Thirty-Second International conference on machine learning: 6-11 July 2015. France: 2015. p. 2483–92.
Lee B, Baek J, Park S, Yoon S. deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks. In: Proceedings of the Seventh ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics: 2-5 October 2016. Seattle: ACM: 2016. p. 434–42.
Xu Z-C, Wang P, Qiu W-R, Xiao X. iss-pc: Identifying splicing sites via physical-chemical properties using deep sparse auto-encoder. Sci Rep. 2017; 7(1):8222.