1.
Aarts E, Verhage M, Veenvliet JV, Dolan CV, Van Der Sluis S: A solution to dependency: using multilevel analysis to accommodate nested data. Nature neuroscience 2014, 17:491.
2.
Consortium G: The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation
in humans. Science 2015, 348:648-660.
3.
Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MD, Niu B,
McLellan MD, Uzunangelov V: Multiplatform analysis of 12 cancer types reveals molecular classification within
and across tissues of origin. Cell 2014, 158:929-944.
4.
Zhang W, Yu Y, Hertwig F, Thierry-Mieg J, Zhang W, Thierry-Mieg D, Wang J, Furlanello
C, Devanarayan V, Cheng J: Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome biology 2015, 16:133.
5.
Su Z, Łabaj PP, Li S, Thierry-Mieg J, Thierry-Mieg D, Shi W, Wang C, Schroth GP, Setterquist
RA, Thompson JF: A comprehensive assessment of RNA-seq accuracy, reproducibility and information content
by the Sequencing Quality Control Consortium. Nature biotechnology 2014, 32:903.
6.
Xu J, Gong B, Wu L, Thakkar S, Hong H, Tong W: Comprehensive assessments of RNA-seq by the SEQC consortium: FDA-led efforts advance
precision medicine. Pharmaceutics 2016, 8:8.
Wu C, Zhou F, Ren J, Li X, Jiang Y, Ma S:
A Selective review of multi-level omics data integration using variable selection. <em>
High-throughput </em>2019,
8:4.
</p>
8.
Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G, Milanesi L:
Methods for the integration of multi-omics data: mathematical aspects. <em>
BMC bioinformatics </em>2016,
17:S15.
</p>
9.
Richardson S, Tseng GC, Sun W:
Statistical methods in integrative genomics.
Annual review of statistics and its application 2016,
3:181-209.
10.
LeCun Y, Bengio Y, Hinton G: Deep learning. nature 2015, 521:436.
11.
Cohen JB, Simi M, Campagne F: GenotypeTensors: Efficient Neural Network Genotype Callers. bioRxiv 2018:338780.
12.
Poplin R, Newburger D, Dijamco J, Nguyen N, Loy D, Gross SS, McLean CY, DePristo MA:
Creating a universal SNP and small indel variant caller with deep neural networks. BioRxiv 2017:092890.
13.
Alipanahi B, Delong A, Weirauch MT, Frey BJ: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology 2015, 33:831.
14.
Zhou J, Troyanskaya OG: Predicting effects of noncoding variants with deep learning–based sequence model. Nature methods 2015, 12:931.
15.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S: Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017, 542:115.
16.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner
K, Madams T, Cuadros J: Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. Jama 2016, 316:2402-2410.
17.
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:160605718 2016.
18.
Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 2012: 1097-1105.
19.
Simard PY, Steinkraus D, Platt JC: Best practices for convolutional neural networks applied to visual document analysis.
In ICDAR. 2003: 958-962.
20.
Hochreiter S, Bengio Y, Frasconi P, Schmidhuber J: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press; 2001.
21.
Hochreiter S, Schmidhuber J: Long short-term memory. Neural computation 1997, 9:1735-1780.
22.
Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E,
Agapow P-M, Zietz M, Hoffman MM: Opportunities and obstacles for deep learning in biology and medicine. bioRxiv 2018:142760.
23.
Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D: Domain separation networks. In Advances in Neural Information Processing Systems. 2016: 343-351.
24.
Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, Zhang Y, Dong Z, Lancashire LJ, Bessarabova
M: An investigation of biomarkers derived from legacy microarray data for their utility
in the RNA-seq era. Genome biology 2014, 15:523.