Fetching the paper…
Reading the bibliography…
The availability of genomic data is essential to progress in biomedical research, personalized medicine, etc.
The distribution of gene ratios for rare mutations
R. A. Fisher · 1931
Earlier work this paper cites.
The significance probability of the Smirnov two-sample test
J. L. Hodges · 1958
Earlier work this paper cites.
Information processing in dynamical systems: Foundations of harmony theory
P. Smolensky · 1986
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Disentangling linkage disequilibrium and linkage from dense single-nucleotide polymorphism trio data
G. M. Clarke and L. R. Cardon · 2005
Earlier work this paper cites.
Non-equilibrium theory of the allele frequency spectrum
S. N. Evans, Y. Shvets, and M. Slatkin · 2007
Earlier work this paper cites.
Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
Earlier work this paper cites.
Genetics in geographically structured populations: defining, estimating and interpreting FST
K. E. Holsinger and B. S. Weir · 2009
Earlier work this paper cites.
Linkage disequilibrium between loci with unknown phase
A. R. Rogers and C. Huff · 2009
Earlier work this paper cites.
Learning your identity and disease from research papers: information leaks in genome wide association study
R. Wang, Y. F. Li, X. Wang, H. Tang, and X. Zhou · 2009
Earlier work this paper cites.
To release or not to release: evaluating information leaks in aggregate human-genome data
X. Zhou, B. Peng, Y. F. Li, Y. Chen, H. Tang, and X. Wang · 2011
Earlier work this paper cites.
Genome-wide association studies
W. S. Bush and J. H. Moore · 2012
Earlier work this paper cites.
On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy
H. K. Im, E. R. Gamazon, D. L. Nicolae, and N. J. Cox · 2012
Earlier work this paper cites.
International HapMap Project
National Human Genome Research Institute · 2012
Earlier work this paper cites.
Identifying personal genomes by surname inference
M. Gymrek, A. L. McGuire, D. Golan, E. Halperin, and Y. Erlich · 2013
Earlier work this paper cites.
Addressing the concerns of the lacks family: quantification of kin genomic privacy
M. Humbert, E. Ayday, J.-P. Hubaux, and A. Telenti · 2013
Earlier work this paper cites.
Privacy-preserving data exploration in genome-wide association studies
A. Johnson and V. Shmatikov · 2013
Earlier work this paper cites.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
A systematic comparison of supervised classifiers
D. R. Amancio, C. H. Comin, D. Casanova, G. Travieso, O. M. Bruno, F. A. Rodrigues, and L. da Fontoura Costa · 2014
Earlier work this paper cites.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
Earlier work this paper cites.
On genomics, kin, and privacy
A. Telenti, E. Ayday, and J. P. Hubaux · 2014
Earlier work this paper cites.
A global reference for human genetic variation
1000 Genomes Project Consortium · 2015
Earlier work this paper cites.
Whole genome sequencing: Revolutionary medicine or privacy nightmare?
E. Ayday, E. De Cristofaro, J.-P. Hubaux, and G. Tsudik · 2015
Earlier work this paper cites.
Inceptionism: Going Deeper into Neural Networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
Earlier work this paper cites.
Privacy In The Genomic Era
M. Naveed, E. Ayday, E. W. Clayton, J. Fellay, C. A. Gunter, J.-P. Hubaux, B. A. Malin, and X. Wang · 2015
Earlier work this paper cites.
Quantifying genomic privacy via inference attack with high-order SNV correlations
S. S. Samani, Z. Huang, E. Ayday, M. Elliot, J. Fellay, J.-P. Hubaux, and Z. Kutalik · 2015
Cited alongside, same era.
Privacy risks from genomic data-sharing beacons
S. S. Shringarpure and C. D. Bustamante · 2015
Cited alongside, same era.
Understanding Neural Networks Through Deep Visualization
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson · 2015
Cited alongside, same era.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies
C. Benner, A. S. Havulinna, M.-R. Järvelin, V. Salomaa, S. Ripatti, and M. Pirinen · 2017
Cited alongside, same era.
Creating Artificial Human Genomes Using Generative Models
B. Yelmen, A. Decelle, L. Ongaro, D. Marnetto, F. Montinaro, C. Furtlehner, L. Pagani, and F. Jay · 2019
Later among the works it cites.
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
S. Biswal, S. Ghosh, J. Duke, B. Malin, W. Stewart, and J. Sun · 2020
Later among the works it cites.
Gs-wgan: A gradient-sanitized approach for learning differentially private generators
D. Chen, T. Orekondy, and M. Fritz · 2020
Later among the works it cites.
GAN-Leaks: A taxonomy of membership inference attacks against GANs
D. Chen, N. Yu, Y. Zhang, and M. Fritz · 2020
Later among the works it cites.
Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data
J. Chen, W. H. Wang, and X. Shi · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generating multi-label discrete patient records using generative adversarial networks
E. Choi, S. Biswal, B. Malin, J. Duke, W. F. Stewart, and J. Sun · 2017
Cited alongside, same era.
Improved Training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
Cited alongside, same era.
Generating and designing DNA with deep generative models
N. Killoran, L. Lee, A. Delong, D. Duvenaud, and B. Frey · 2017
Cited alongside, same era.
Rényi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Addressing Beacon re-identification attacks: quantification and mitigation of privacy risks
J. L. Raisaro, F. Tramer, Z. Ji, D. Bu, Y. Zhao, K. Carey, D. Lloyd, H. Sofia, D. Baker, P. Flicek, et al · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Genome Privacy: Challenges, Technical Approaches to Mitigate Risk, and Ethical Considerations in the United States
S. Wang, X. Jiang, S. Singh, R. Marmor, L. Bonomi, D. Fox, M. Dow, and L. Ohno-Machado · 2017
Cited alongside, same era.
Synthetic data – All the perks without the risk?
H. Elias · 2020
Later among the works it cites.
Variant classification
Ensembl Variation · 2020
Later among the works it cites.
What is Precision Medicine?
Genetics Home Reference · 2020
Later among the works it cites.
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
K. Leino and M. Fredrikson · 2020
Later among the works it cites.
How Artificial Intelligence Can Revolutionize Healthcare
P. Sadrach · 2020
Later among the works it cites.
Synthetic Data – A Privacy Mirage
T. Stadler, B. Oprisanu, and C. Troncoso · 2020
Later among the works it cites.
A. Torfi and E. A. Fox · 2020
Later among the works it cites.
Differentially Private Synthetic Medical Data Generation using Convolutional GANs
A. Torfi, E. A. Fox, and C. K. Reddy · 2020
Later among the works it cites.
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
A. Tucker, Z. Wang, Y. Rotalinti, and P. Myles · 2020
Later among the works it cites.
Press Release – New synthetic datasets to assist COVID-19 and cardiovascular research
UK Government · 2020
Later among the works it cites.
Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)
J. Yoon, L. Drumright, and M. Schaar · 2020
Later among the works it cites.
What are genome wide association studies (GWAS)?
EMBL-EBI Training · 2021
Closest in time.
Practical blind membership inference attack via differential comparisons
B. Hui, Y. Yang, H. Yuan, P. Burlina, N. Z. Gong, and Y. Cao · 2021
Closest in time.
Generating and designing DNA
N. Killoran · 2021
Closest in time.
Genome-Wide Association Studies Fact Sheet
National Human Genome Research Institute · 2021
Closest in time.
A&E Synthetic Data
NHS England · 2021
Closest in time.
Genomic Data Sharing
NIH · 2021
Closest in time.
Genetic maps for the 1000 Genomes Project variants
J. Pickrell · 2021
Closest in time.
Finding hidden treasures in summary statistics from genome-wide association studies
F. Privé, Z. Zhu, and B. J. Vilhjalmsson · 2021
Closest in time.