GMFR-CNN: An integration of gapped motif feature representation and deep learning approach for enhancer prediction

ACM International Conference Proceeding Series

Volumn , Issue , 2016, Pages 41-47

  Wong, Yu Shiong ^a   Lee, Nung Kion ^a   Omar, Norshafarina ^a  

^a UNIVERSITI MALAYSIA SARAWAK   (Malaysia)

Author keywords

Convolution neural network; Enhancer motifs; Gapped motif feature representation

Indexed keywords

BIOINFORMATICS; CONVOLUTION; DEEP LEARNING; FORECASTING; GENE EXPRESSION; GENES; IMAGE RECOGNITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; TRANSCRIPTION;

COMPARATIVE STUDIES; CONVOLUTION NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORK; ENHANCER MOTIFS; FEATURE REPRESENTATION; LEARNING TECHNIQUES; MACHINE LEARNING TECHNIQUES; PREDICTION ACCURACY;

DEEP NEURAL NETWORKS;

EID: 85015246115     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3029375.3029380     Document Type: Conference Paper

References (26)

1. Pennacchio, L. A., Bickmore, W., Dean, A., Nobrega, M. A., and Bejerano, G. 2013. Enhancers: Five essential questions. Nature Reviews Genetics 14, 4 (2013), 288-295. DOI= http://dx.doi.org/10.1038/nrg3458.
2. Shlyueva, D., Stampfel, G., and Stark, A. 2014. Transcriptional enhancers: From properties to genome-wide predictions. Nature Reviews Genetics 15, 4 (November 2014), 272-286. DOI= http://dx.doi.org/10.1038/nrg3682.
3. Lee, D., Karchin, R., and Beer, M. A. 2011. Discriminative prediction of mammalian enhancers from DNA sequence. Genome Research 21, 12 (2011), 2167-2180. DOI= http://dx.doi.org/10.1101/gr.121905.111.
4. Ghandi, M., Lee, D., Mohammad-Noori, M., and Beer, M. A. 2014. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Computational Biology 10, 7 (2014). DOI= http://dx.doi.org/10.1371/journal.pcbi.1003711.
5. Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J. 2015. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology 33, 8 (2015), 831-838. DOI= http://dx.doi.org/10.1038/nbt.3300.
6. Fletez-Brant, C., Lee, D., McCallion, A. S., and Beer, M. A. 2013. Kmer-SVM: A web server for identifying predictive regulatory sequence features in genomic data sets. Nucleic Acids Research 41, W1 (2013). DOI= http://dx.doi.org/10.1093/nar/gkt519.
7. Pervouchine, D. D. 2014. IRBIS: A systematic search for conserved complementarity. RNA 20, 10 (2014), 1519-1531. DOI= http://dx.doi.org/10.1261/rna.045088.114.
8. Kelley, D. R., Snoek, J., and Rinn, J. L. 2016. Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research 26, 7 (March 2016), 990-999. DOI= http://dx.doi.org/10.1101/gr.200535.115.
9. Kleftogiannis, D., Kalnis, P., and Bajic, V. B. 2014. DEEP: A general computational framework for predicting enhancers. Nucleic Acids Research 43, 1 (May 2014). DOI= http://dx.doi.org/10.1093/nar/gku1058.
10. Firpi, H. A., Ucar, D., and Tan, K. 2010.Discover regulatory DNA elements using chromatin signatures and artificial neural network. Bioinformatics 26, 13 (July 2010), 1579-1586. DOI= http://dx.doi.org/10.1093/bioinformatics/btq248.
11. Annala, M., Laurila, K., Lähdesmäki, H., and Nykter, M. 2011. A linear model for transcription factor binding affinity prediction in protein binding microarrays. PLoS ONE 6, 5 (2011). DOI= http://dx.doi.org/10.1371/journal.pone.0020059.
12. Elemento, O. and Tavazoie, S. 2005. Fast and systematic genome-wide discovery of conserved regulatory elements using a non-alignment based approach. Genome Biology 6, 2 (2005). DOI= http://dx.doi.org/10.1186/gb-2005-6-2-r18.
13. Berger, M. F. and Bulyk, M. L. 2009. Universal proteinbinding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors. Nature Protocols 4, 3 (2009), 393-411. DOI= http://dx.doi.org/10.1038/nprot.2008.195.
14. Hong, M., Fitzgerald, M. X., Harper, S., Luo, C., Speicher, D. W., and Marmorstein, R. 2008. Structural basis for dimerization in DNA recognition by Gal4. Structure 16 , 7 (2008), 1019-1026. DOI= http://dx.doi.org/10.2210/pdb3coq/pdb.
15. Riley, T., Sontag, E., Chen, P., and Levine, A. 2008. Transcriptional control of human p53-regulated genes. Nature Reviews Molecular Cell Biology 9, 5 (2008), 402-412. DOI= http://dx.doi.org/10.1038/nrm2395.
16. Carroll, J. S., Meyer, C. A., Song, J., W., Geistlinger, T. R., Eeckhoute, J., Brodsky, A. S., Keeton, E. K., Fertuck, K. C., Hall, G. F., and Wang, Q. 2006. Genome-wide analysis of estrogen receptor binding sites. Nature Genetics 38, 11 (2006), 1289-1297. DOI: Http://dx.doi.org/10.1038/ng1901.
17. Leibovich, L. and Yakhini, Z. 2012. Efficient motif search in ranked lists and applications to variable gap motifs. Nucleic Acids Research 40, 13 (2012), 5832-5847. DOI= http://dx.doi.org/10.1093/nar/gks206.
18. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. 1998. Gradient-based learning applied to document recognition. In Proceedings of the IEEE 86, 11 (1998), 2278-2324. DOI= http://dx.doi.org/10.1109/5.726791.
19. Fukushima, K. and Miyake, S. 1982. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 15, 6 (1982), 455- 469. DOI= http://dx.doi.org/10.1016/0031-3203(82)90024-3.
20. Haykin, S. S. 2009. Neural networks and learning machines, New York: Prentice Hall/Pearson.
21. Guigo, R. An Introduction to Position Specific Scoring Matrices. Retrieved August 30, 2016 from http://bioinformatica.upf.edu/T13/makeprofile.html.
22. Juszczak, P., Tax, D., and Duin, R. P. 2002. Feature scaling in support vector data description. In Proc. ASCI, 95-102.
23. Manning, C. D., Raghavan, P., and Schütze, H. 2008. Introduction to information retrieval, New York: Cambridge University Press.
24. Zhu, W., Zeng, N., and Wang, N. 2010.Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. In NESUG proceedings: Health care and life sciences, Baltimore, Maryland, 1-9.
25. Palm, R. B. 2012. Prediction as a candidate for learning deep hierarchical models of data. Technical University of Denmark, DTU Informatics, Denmark.
26. Ahn, S., Huang, C.L., Ozkumur, E., Zhang, X., Chinnala, J., Yalcin, A., Bandyopadhyay, S., Russek, S.J., Ünlü, M.S., DeLisi, C. and Irani, R.J., 2012. TATA binding proteins can recognize nontraditional DNA sequences. Biophysical Journal 103, 7 (2012), 1510-1517. DOI= .