Preprint/submitted

[38] W. Li, K. Dao Duc, Y. Park, Optimal transport based transfer learning for Electronic Health Records (under review)

[37] W. Li, J. Mirone, A. Prasad, N. Miolane, C. Legrand and K. Dao Duc, Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets, (under review) biorXiv

[36] K. Flanagan, S. Pelech, Y. Av-Gay and K. Dao Duc, CAT PETR: A Graphical User Interface for Differential Analysis of Phosphorylation Data (under review), biorXiv

[35] A. Tajmir Riahi, G. Woollard, F. Poitevin, A. Condon, K. Dao Duc, AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps (under review), arXiv 

In press or published

[34] W. Li, A. Prasad, N. Miolane, K. Dao Duc (2023), “Using a Riemannian elastic metric for statistical analysis of tumor cell shape heterogeneity”, (accepted in Proceedings of the 6th International Conference on Geometric Science of Information)

[33] F.E. Acosta, S., Sanborn, K., Dao Duc, M., Madhav, M., and N. Miolane (2023), “Quantifying Local Extrinsic Curvature in Neural Manifolds”, (accepted in CVPR TAG workshop 2023) arXiv

[32] S. Yu, S. Srebnik, K. Dao Duc (2023), “Geometric differences in the ribosome exit tunnel impact the escape of small nascent proteins” Biophysical Journal,122 (1), 20-29 link, biorXiv

[31] A. Kushner, A.S. Petrov, K. Dao Duc, (2022) “RiboXYZ: A comprehensive database for ribosome structures”,  Nucleic Acids Research, gkac939, link, link to database server

[30] C. Zhang, K. Dao Duc, A. Condon (2022), “Visualizing DNA reaction trajectories with deep graph embedding approaches”,  2022 NeurIPS workshop on Machine Learning in Structural Biology

[29] G. Woollard, S. Shekarforoush, F. Wood, M. Brubaker, K. Dao Duc (2022) “Physics aware inference for the cryo-EM inverse problem: anisotropic network model heterogeneity, global pose and microscope defocus” 2022 NeurIPS workshop on Machine Learning in Structural Biology

[28] A. Tajmir Riahi, G. Woollard, F. Poitevin, A. Condon, K. Dao Duc, (2022), “3D alignment of cryogenic electron microscopy density maps by minimizing their Wasserstein distance”, 2022 NeurIPS workshop on Machine Learning in Structural Biology

[27] T. Niederhauser, A. Lester, N. Miolane, K. Dao Duc, M. S. Madhav, (2022) “Testing different hypothesis of geometric representation from simulated place field cells recordings”, 2022 NeurIPS workshop on Symmetry and Geometry in Neural Representations (extended abstract), arXiv

[26] K. Flanagan, W. Li. E. Greenblatt, K. Dao Duc, (2022) “End-to-end pipeline for differential analysis of pausing in ribosome profiling data”, STAR protocols 3.3: 101605. (link)

[25] K. Flanagan, A. Baradaran-Heravi, K. Dao Duc, Q. Yin, A. C. Spradling, and E. J. Greenblatt (2022), “FMRP-dependent production of large dosage-sensitive proteins is highly conserved”, Genetics, iyac094, https://doi.org/10.1093/genetics/iyac094 (Selected Featured Article in Genetics)

[24] N. Legendre, K. Dao Duc*, N. Miolane, (2022) “Defining an action of $SO(d)$-rotations on images generated by projections of $d$-dimensional objects: Applications to pose inference with Geometric VAEs”. 28e colloque du Groupe de Recherche et d’Etudes du Traitement du Signal et des Images (GRETSI 2022) (* co-senior author), arXiv

[23] A. Ecoffet, G. Woollard, A. Kushner, F. Poitevin, K. Dao Duc, (2022) “Application of transport-based metric for continuous interpolation between cryo-EM density maps”. AIMS Mathematics, 2022, 7(1): 986-999. doi: 10.3934/math.2022059

[22] F. Tuorto, K. Dao Duc, C. Legrand (2022) “Analysis of Ribosome Profiling Data” (book chapter), The Integrated Stress Response, Methods in Molecular Biology, Springer, link

[21] N. Miolane, et al. (2021) “ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results.”  arXiv:2108.09810 link.

[20] D.D. Erdmann-Pham, W. Son, K. Dao Duc*,  Y.S. Song, (2021), “EGGTART: A computational tool to quantify the dynamics of biophysical transport from the inhomogeneous l-TASEP” (* co-senior author), Biophysical Journal, 120, 1309–1313 arXiv

[19] A. Ecoffet, F. Poitevin, K. Dao Duc, (2020), “MorphOT: Transport-based interpolation between EM maps with UCSF ChimeraX”, Bioinformatics, btaa1019, link, biorXiv

[18] F. Poitevin, A. Kushner, X. Li, K. Dao Duc (2020), “Structural heterogeneities of the ribosome: New frontiers and opportunities for cryo-EM”, Molecules, 25, 4262. pdf, link

[17]  D.D. Erdmann-Pham, K. Dao Duc, Y.S. Song (2020), “The key parameters that govern translation efficiency”, Cell Systems, link, arXiv, link 2

[16] K. Dao Duc, S. Batra, N. Bhattacharya, J.H.D. Cate and Y.S. Song (2019) “Differences in the path to exit the ribosome across the three domains of life” Nucleic Acids Research, gkz106, (F1000 recommendation) link

[15] K. Dao Duc, Y.S. Song (2018) “The impact of ribosomal interference, codon usage, and exit tunnel interactions on translation elongation rate variation”. PLoS Genetics 14(1): e1007166. link (PLoS Genetics top 10% cited)

[14] K. Dao Duc, Z.H. Saleem, Y.S. Song (2018) “Theoretical analysis of the distribution of isolated particles in the TASEP: Application to mRNA translation rate estimation.” Physical Review E 97, 012106  (selected as Editor’s suggestion) link

[13] N. Rouach, K. Dao Duc*, J. Sibille*, D. Holcman (2018) “Dynamics of ion fluxes between neurons, astrocytes and the extracellular space during neurotransmission” (Review), 4(1), 1-18 Opera Medica et Physiologica) (* equal contribution) link

[12] M. Wang, K. Dao Duc, J. Fischer, Y.S. Song (2017) “Operator norm inequalities between tensor unfoldings on the partition lattice.” Linear Algebra and its Applications 520: 44-66 link

[11] K. Dao Duc, Z. Schuss, and D. Holcman (2016) “Oscillatory Survival Probability: Analytical and Numerical Study of a Non-Poissonian Exit Time.” SIAM Multiscale Modeling & Simulation 14.2 : 772-798. link

[10] J. Sibille*, K. Dao Duc*, D. Holcman, N. Rouach (2015) “The Neuroglial Potassium Cycle during Neurotransmission: Role of Kir4.1 Channels.” PLoS Computational Biology 11(3): e1004137. link (* equal contribution)

[9] K. Dao Duc, P. Parutto, X. Chen, J. Epsztein, A. Konnerth, D. Holcman, (2015) “Synaptic Dynamics and Neuronal Network Connectivity are reflected in the Distribution of Times in Up states”, Frontiers in Computational Neuroscience, 9, 96. link

[8] K. Dao Duc, C. Lee, P. Parutto, D. Cohen, M. Segal, N. Rouach, et al. (2015) “Bursting Reverberation as a Multiscale Neuronal Network Process Driven by Synaptic Depression-Facilitation”. PLoS ONE 10(5): e0124694. link

[7] D. Holcman, K. Dao Duc, A. Jones, H. Byrne, K. Burrage, (2015), “Post-transcriptional regulation in the nucleus and cytoplasm: study of mean time to treshold (MTT) and narrow escape problem”, Journal of mathematical biology, 70.4: 805-828. link

[6] K. Dao Duc, Z. Schuss, D. Holcman, (2014) “Oscillatory decay of the survival probability of activated diffusion across a limit cycle”, Physical Review E 89.3 (2014): 030101 link

[5] K. Dao Duc, D. Holcman, (2013), “Computing the length of the shortest telomeres”, Physical Review Letters, 111, 228104 (highlighted for a Physics viewpoint) link

[4] Z. Xu, K. Dao Duc, D. Holcman, T. Teixeira (2013), “The length of the shortest telomere as the major determinant of the onset of replicative senescence”, Genetics, 194, pp. 847-857 link

[3] K. Dao Duc, D. Holcman (2012), “Using default constraints of the spindle assembly checkpoint to estimate the associated chemical rates”, BMC Biophysics; 5(1):1. link

[2] K. Dao Duc, D. Holcman (2010), “Threshold activation for stochastic chemical reactions in microdomains”, Physical Review E. 81 (4(1)): 041107 link

[1] K. Dao Duc, P. Auger, T. Nguyen Huu (2008), “Predator density dependent prey dispersal in a patchy environment with a refuge for the prey”, South African Journal of Science, vol. 104, no5-6, pp. 180-184 link