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Items: 6

1.

Opportunities and obstacles for deep learning in biology and medicine.

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS.

J R Soc Interface. 2018 Apr;15(141). pii: 20170387. doi: 10.1098/rsif.2017.0387. Review.

2.

Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis.

Beaulieu-Jones BK, Lavage DR, Snyder JW, Moore JH, Pendergrass SA, Bauer CR.

JMIR Med Inform. 2018 Feb 23;6(1):e11. doi: 10.2196/medinform.8960.

3.
4.

Reproducibility of computational workflows is automated using continuous analysis.

Beaulieu-Jones BK, Greene CS.

Nat Biotechnol. 2017 Apr;35(4):342-346. doi: 10.1038/nbt.3780. Epub 2017 Mar 13.

5.

MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS.

Beaulieu-Jones BK, Moore JH.

Pac Symp Biocomput. 2017;22:207-218. doi: 10.1142/9789813207813_0021.

6.

Semi-supervised learning of the electronic health record for phenotype stratification.

Beaulieu-Jones BK, Greene CS; Pooled Resource Open-Access ALS Clinical Trials Consortium.

J Biomed Inform. 2016 Dec;64:168-178. doi: 10.1016/j.jbi.2016.10.007. Epub 2016 Oct 12.

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