Page 286 - Artificial Intelligence in the Age of Neural Networks and Brain Computing

References 279

[15] B. Wang, A.M. Mezlini, F. Demir, M. Fiume, Z. Tu, M. Brudno, B. Haibe-Kains,
A. Goldenberg, Similarity network fusion for aggregating data types on a genomic
scale, Nature Methods 11 (3) (2014) 333e337. Nature Publishing Group.
[16] A. Serra, M. Fratello, V. Fortino, G. Raiconi, R. Tagliaferri, D. Greco, MVDA: A Multi-
view genomic data integration methodology, BMC Bioinformatics 16 (1) (2015) 261.
BioMed Central Ltd.
[17] S.H. Sleigh, C.L. Barton, Repurposing strategies for therapeutics, Pharmaceutical
Medicine 24 (3) (2010) 151e159. Springer.
[18] J.A. DiMasi, R.W. Hansen, H.G. Grabowski, L. Lasagna, Cost of innovation in the phar-
maceutical industry, Journal of Health Economics 10 (2) (1991) 107e142. Elsevier.
[19] K.A. O’Connor, B.L. Roth, Finding new tricks for old drugs: an efficient route for
public-sector drug discovery, Nature Reviews Drug Discovery 4 (12) (2005) 1005e1014.
Nature Publishing Group.
[20] J.T. Dudley, M. Sirota, M. Shenoy, R.K. Pai, S. Roedder, A.P. Chiang, A.A. Morgan,
M.M. Sarwal, P. Jay Pasricha, A.J. Butte, Computational repositioning of the anticon-
vulsant topiramate for inflammatory bowel disease, Science Translational Medicine 3
(96) (2011) 96ra76. American Association for the Advancement of Science.
[21] A. Gottlieb, G.Y. Stein, E. Ruppin, R. Sharan, PREDICT: a method for inferring novel
drug indications with application to personalized medicine, Molecular Systems Biology
7 (1) (2011) 496. EMBO Press.
[22] P. Sanseau, P. Agarwal, M.R. Barnes, T. Pastinen, J. Brent Richards, L.R. Cardon,
V. Mooser, Use of genome-wide association studies for drug repositioning, Nature
Biotechnology 30 (4) (2012) 317e320. Nature Research.
[23] F. Iorio, R. Bosotti, E. Scacheri, V. Belcastro, P. Mithbaokar, R. Ferriero, L. Murino,
et al., Discovery of drug mode of action and drug repositioning from transcriptional
responses, Proceedings of the National Academy of Sciences 107 (33) (2010)
14621e14626. National Acad Sciences.
[24] N. Smirnov, Table for estimating the goodness of fit of empirical distributions, The
Annals of Mathematical Statistics 19 (2) (1948) 279e281. JSTOR.
[25] F. Napolitano, Y. Zhao, V.M. Moreira, R. Tagliaferri, J. Kere, M. D’Amato, D. Greco,
Drug repositioning: a machine-learning approach through data integration, Journal of
Cheminformatics 5 (1) (2013) 30. Springer.
[26] B. Thirion, G. Varoquaux, E. Dohmatob, J.-B. Poline, Which FMRI clustering gives
good brain parcellations? Frontiers in Neuroscience 8 (2014). Frontiers Media SA.
[27] M.F. Glasser, T.S. Coalson, E.C. Robinson, C.D. Hacker, J. Harwell, E. Yacoub,
K. Ugurbil, et al., A multi-modal parcellation of human cerebral cortex, Nature
(2016). Nature Research.
[28] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015) 436e444.
Nature Research.
[29] K. Chaudhary, O.B. Poirion, L. Lu, L. Garmire, Deep learning based multi-omics inte-
gration robustly predicts survival in liver cancer, BioRxiv, Cold Spring Harbor Labs
Journals (2017) 114892.
[30] Y. Bengio, et al., Learning deep architectures for Ai, Foundations and Trends in
Machine Learning 2 (1) (2009) 1e127. Now Publishers, Inc.
[31] S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M.J. Fulham, others, Multi-
modal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease,
IEEE Transactions on Biomedical Engineering 62 (4) (2015) 1132e1140. IEEE.