**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