1 |
P. Mamoshina, A. Vieira, E. Putin and A. Zhavoronkov, "Applications of deep learning in biomedicine," Molecular pharmaceutics, vol. 13, p. 1445-1454, 2016.
DOI
|
2 |
B. P. Lewis, I.-h. Shih, M. W. Jones-Rhoades, D. P. Bartel and C. B. Burge, "Prediction of mammalian microRNA targets," Cell, vol. 115, p. 787-798, 2003.
DOI
|
3 |
Y. Chen, Y. Li, R. Narayan, A. Subramanian and X. Xie, "Gene expression inference with deep learning," Bioinformatics, vol. 32, p. 1832-1839, 2016.
DOI
|
4 |
J. Lanchantin, R. Singh, B. Wang and Y. Qi, "Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks," in Pacific Symposium on Biocomputing 2017, 2017.
|
5 |
S. S. Sahu, "Analysis of Genomic and Proteomic Signals Using Signal Processing and Soft Computing Techniques," 2011.
|
6 |
G. De Clercq, "DEEP LEARNING FOR CLASSIFICATION OF DNA FUNCTIONAL SEQUENCES," 2019.
|
7 |
N. Mughees, S. A. Mohsin, A. Mughees and A. Mughees, "Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting," Expert Systems with Applications, vol. 175, p. 114844, 2021.
DOI
|
8 |
A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander and others, "Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles," Proceedings of the National Academy of Sciences, vol. 102, p. 15545-15550, 2005.
DOI
|
9 |
S. Li, P. P. Labaj, P. Zumbo, P. Sykacek, W. Shi, L. Shi, J. Phan, P.-Y. Wu, M. Wang, C. Wang and others, "Detecting and correcting systematic variation in large-scale RNA sequencing data," Nature biotechnology, vol. 32, p. 888-895, 2014.
DOI
|
10 |
Y. A. Abass and S. A. Adeshina, "Deep Learning Methodologies for Genomic Data Prediction," Journal of Artificial Intelligence for Medical Sciences, 2021.
|
11 |
A. Arbaaeen and A. Shah, "Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review," Information, vol. 12, p. 200, 2021.
DOI
|
12 |
K. Zarringhalam, D. Degras, C. Brockel and D. Ziemek, "Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes," Scientific reports, vol. 8, p. 1-10, 2018.
|
13 |
D. E. Beaudoin, N. Longo, R. A. Logan, J. P. Jones and J. A. Mitchell, "Using information prescriptions to refer patients with metabolic conditions to the Genetics Home Reference website," Journal of the Medical Library Association: JMLA, vol. 99, p. 70, 2011.
DOI
|
14 |
Y. Bengio, A. Courville and P. Vincent, "Representation learning: A review and new perspectives," IEEE transactions on pattern analysis and machine intelligence, vol. 35, p. 1798-1828, 2013.
DOI
|
15 |
N. S. Madhukar and O. Elemento, "Bioinformatics approaches to predict drug responses from genomic sequencing," Cancer Systems Biology, p. 277-296, 2018.
|
16 |
S. Tiwari, S. Ramachandran, A. Bhattacharya, S. Bhattacharya and R. Ramaswamy, "Prediction of probable genes by Fourier analysis of genomic sequences," Bioinformatics, vol. 13, p. 263-270, 1997.
DOI
|
17 |
R. Singh, J. Lanchantin, G. Robins and Y. Qi, "DeepChrome: deep-learning for predicting gene expression from histone modifications," Bioinformatics, vol. 32, p. i639-i648, 2016.
DOI
|
18 |
B. M. Kuenzi, J. Park, S. H. Fong, K. S. Sanchez, J. Lee, J. F. Kreisberg, J. Ma and T. Ideker, "Predicting drug response and synergy using a deep learning model of human cancer cells," Cancer cell, vol. 38, p. 672-684, 2020.
DOI
|
19 |
J. Schmidhuber, "Deep learning in neural networks: An overview," Neural networks, vol. 61, p. 85-117, 2015.
DOI
|
20 |
J. Ni, P. Cozzi, J. Beretov, W. Duan, J. Bucci, P. Graham and Y. Li, "Epithelial cell adhesion molecule (EpCAM) is involved in prostate cancer chemotherapy/radiotherapy response in vivo," BMC cancer, vol. 18, p. 1-12, 2018.
DOI
|
21 |
D. Anastassiou, "Genomic signal processing," IEEE signal processing magazine, vol. 18, p. 8-20, 2001.
DOI
|
22 |
H. a. S. M. a. S. M. a. G. F. Saberkari, "Prediction of protein coding regions in DNA sequences using signal processing methods," in 2012 IEEE Symposium on Industrial Electronics and Applications, 2012.
|
23 |
I. Goodfellow, Y. Bengio and A. Courville, Deep learning, MIT press, 2016.
|
24 |
E. S. Lander, L. M. Linton, B. Birren, C. Nusbaum, M. C. Zody, J. Baldwin, K. Devon, K. Dewar, M. Doyle, W. FitzHugh and others, "Initial sequencing and analysis of the human genome," 2001.
|
25 |
Y. Miura, Y. Sakurai and T. Endo, "O-GlcNAc modification affects the ATM-mediated DNA damage response," Biochimica et Biophysica Acta (BBA)-General Subjects, vol. 1820, p. 1678-1685, 2012.
DOI
|
26 |
C. L. M. Marcelis and A. P. M. de Brouwer, "Feingold syndrome 1," 2019.
|
27 |
E. Castro and R. Eeles, "The role of BRCA1 and BRCA2 in prostate cancer," Asian journal of andrology, vol. 14, p. 409, 2012.
DOI
|
28 |
S. Sunyaev, J. Hanke, A. Aydin, U. Wirkner, I. Zastrow, J. Reich and P. Bork, "Prediction of nonsynonymous single nucleotide polymorphisms in human diseaseassociated genes," Journal of molecular medicine, vol. 77, p. 754-760, 1999.
DOI
|
29 |
H. Saberkari, M. Shamsi and M. H. Sedaaghi, "Identification of genomic islands in DNA sequences using a non-DSP technique based on the Z-Curve," in 11th Iranian Conference on Intelligent Systems (ICIS 2013) February 27th & 28th, 2013.
|
30 |
A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, p. 84-90, 2017.
DOI
|
31 |
C. Olah, "Understanding lstm networks-colah's blog," Colah. github. io, 2015.
|
32 |
H. P. Desai, A. P. Parameshwaran, R. Sunderraman and M. Weeks, "Comparative study using neural networks for 16S ribosomal gene classification," Journal of Computational Biology, vol. 27, p. 248-258, 2020.
DOI
|
33 |
M. Axelson-Fisk, "Comparative Gene Finding," in Comparative Gene Finding, Springer, 2010, p. 157-180.
|
34 |
L. Fu, Q. Peng and L. Chai, "Predicting dna methylation states with hybrid information based deep-learning model," IEEE/ACM transactions on computational biology and bioinformatics, vol. 17, p. 1721-1728, 2019.
DOI
|
35 |
R. Lopez, J. Regier, M. B. Cole, M. I. Jordan and N. Yosef, "Deep generative modeling for single-cell transcriptomics," Nature methods, vol. 15, p. 1053-1058, 2018.
DOI
|
36 |
S. Park, S. Min, H. Choi and S. Yoon, "deepMiRGene: Deep neural network based precursor microrna prediction," arXiv preprint arXiv:1605.00017, 2016.
|
37 |
B. Lee, J. Baek, S. Park and S. Yoon, "deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks," in Proceedings of the 7th ACM international conference on bioinformatics, computational biology, and health informatics, 2016.
|
38 |
Y.-J. Shen and S.-G. Huang, "Improve survival prediction using principal components of gene expression data," Genomics, proteomics & bioinformatics, vol. 4, p. 110-119, 2006.
DOI
|
39 |
R. C. Edgar, "Search and clustering orders of magnitude faster than BLAST," Bioinformatics, vol. 26, p. 2460-2461, 2010.
DOI
|
40 |
L. Pinello, G. Lo Bosco and G.-C. Yuan, "Applications of alignment-free methods in epigenomics," Briefings in Bioinformatics, vol. 15, p. 419-430, 2014.
DOI
|
41 |
D. Urda, J. Montes-Torres, F. Moreno, L. Franco and J. M. Jerez, "Deep learning to analyze RNA-seq gene expression data," in International work-conference on artificial neural networks, 2017.
|
42 |
K. Tutlewska, J. Lubinski and G. Kurzawski, "Germline deletions in the EPCAM gene as a cause of Lynch syndrome-literature review," Hereditary cancer in clinical practice, vol. 11, p. 1-9, 2013.
DOI
|
43 |
S. Siami-Namini, N. Tavakoli and A. S. Namin, "A comparative analysis of forecasting financial time series using arima, lstm, and bilstm," arXiv preprint arXiv:1911.09512, 2019.
|
44 |
D. P. Snustad and M. J. Simmons, Principles of genetics, John Wiley & Sons, 2015.
|
45 |
J. S. Bridle, "Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition," in Neurocomputing, Springer, 1990, p. 227-236.
|
46 |
G. L. Bosco, "Alignment free dissimilarities for nucleosome classification," in International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2015.
|
47 |
T. Yue and H. Wang, "Deep learning for genomics: A concise overview," arXiv preprint arXiv:1802.00810, 2018.
|