Preprint/submitted

• G. Woollard, D. Herreros, M. Li, P. Cossio, K. Dao Duc, Improving Cryo-EM Optimization Robustness with a Transport-Based Objective biorXiV
• A. Tajmir Riahi, K. Dao Duc, The Joint Gromov Wasserstein Objective for multiple object matching, arXiv
• W. Zhao, D. Sutherland, K. Dao Duc, Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel, biorXiV
• W. Li, Y. Park, K. Dao Duc, Wasserstein projection distance for fairness testing of regression models arXiv
• M.A. Astore, G. Woollard, D. Silva-Sanchez, et al., The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge bioRxiv 2025.07.18.665582; doi: https://doi.org/10.1101/2025.07.18.665582
• S. Yu, A. Kushner, S. Srebnik, K. Dao Duc, Detection of  prokaryotic-like ribosome exit tunnels across eukaryotic kingdoms, biorXiX
• C. Soubrier, A. Madzvamuse, H.A. Eskandarian, K. Dao Duc, Quantifying the organization and dynamics of M. smegmatis morphology  from Long-Term Time-Lapse Atomic Force Microscopy biorXiV
• L. Cavalcante, H. Wittman, A. Giles, G. J. Barônio, J. P. Mota, A. C. A. Farias, S. K. Dhaka, K. Dao Duc, and I. Siddique, Diverse marketing channels boost unconventional food plant diversity

In press or published

[49] S. Yu, A. Kushner, E. Teasell, W. Zhao, S. Srebnik, K. Dao Duc, Advanced coarse grained model for fast simulation of nascent polypeptide chain within the ribosome, Biophysical Journal, 2025, DOI: 10.1016/j.bpj.2025.10.044, biorXiV

[48] W. Li, S. Ahmed, Y. Park, K. Dao Duc, Transport-based transfer learning on Electronic Health Records: Application to detection of treatment disparities, Journal of the American Medical Informatics Association, 2025; ocaf134, https://doi.org/10.1093/jamia/ocaf134

[47] C. Zhang, A. Condon, K. Dao Duc, Struc2map: Improving synthetic Cryo-EM Density Maps with Generative Adversarial Networks, Bioinformatics Advances, Volume 5, Issue 1, 2025, vbaf179, https://doi.org/10.1093/bioadv/vbaf179

[46] C. Zhang, K. Dao Duc, CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets (2025), ICML 2025 Generative AI and Biology (GenBio) workshop  arXiv

[45] C. Zhang, A. Condon, K. Dao Duc, A comprehensive survey and benchmark of deep learning-based methods for atomic model building from cryo-electron microscopy density maps, Briefings in Bioinformatics, Volume 26, Issue 4, July 2025, bbaf322, https://doi.org/10.1093/bib/bbaf322

[44] A. Tajmir Riahi, C. Zhang, J. Chen, A. Condon, K. Dao Duc, “Alignment of Partially Overlapping Cryo-EM Maps Using Unbalanced Gromov-Wasserstein Divergence” (2025), PRX Life 3 (2), 023003, https://doi.org/10.1103/PRXLife.3.023003

[43] G. Woollard, W. Zhou, E. T. Thiede, C. Lin, N. Grigorieff, P. Cossio, K. Dao Duc and S. M. Hanson, “InstaMap: Instant-NGP for Cryo-EM Density Maps” (2025), Acta Crystallographica. Section D, Structural Biology. https://doi.org/10.1107/s2059798325002025

[42] Li, W., Prasad, A., Miolane, N. and Dao Duc, K., “Unveiling cellular morphology: statistical analysis using a Riemannian elastic metric in cancer cell image datasets.” Information Geometry (2024). https://doi.org/10.1007/s41884-024-00145-0W.

[41] S. Peña-Díaz, JD. Chao, C. Rens, H. Haghdadi, K. Flanagan, M. Ko, A. Richter, K. Dao Duc, S. Pelech, and Y. Av-Gay (2024) “Glycogen Synthase Kinase 3 controls Mycobacterium tuberculosis Infection”, iScience Volume 27, Issue 8,  2024,  110555,  ISSN 2589-0042,  https://doi.org/10.1016/j.isci.2024.110555.

[40] C. Soubrier, E. Foxall, L. Ciandrini, K. Dao Duc (2024), “Optimal control of ribosome population for gene expression under periodic nutrient intake”, J. R. Soc. Interface. 21:20230652 http://doi.org/10.1098/rsif.2023.0652, arXiv

[39] C. Zhang, J. Lovrod, B. Beronov, K. Dao Duc, A. Condon (2024), “ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings“,  Proceedings of 18th Machine Learning in Computational Biology meeting, PMLR 240:148-162, arXiv

[38] A. Tajmir Riahi, C. Zhang, J.Chen, A. Condon, K. Dao Duc (2023), “EMPOT: partial alignment of density maps and atomic model fitting using unbalanced Gromov-Wasserstein divergence”, 2023 NeurIPS workshop on Machine Learning in Structural Biology, arXiv

[37] A. Tajmir Riahi, G. Woollard, F. Poitevin, A. Condon, K. Dao Duc (2023), “AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps”  IEEE/ACM Transactions in Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2023.3327633 arXiv

[36] W. Li, J. Mirone, A. Prasad, N. Miolane, C. Legrand and K. Dao Duc (2023), “Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets”, Frontiers in Bioinformatics. 3:1211819. doi: 10.3389/fbinf.2023.1211819. PMID: 37637212; PMCID: PMC10448701. biorXiv

[35] K. Flanagan, S. Pelech, Y. Av-Gay and K. Dao Duc (2023), “CAT PETR: A Graphical User Interface for Differential Analysis of Phosphorylation Data”, Statistical Applications in Genetics and Molecular Biology. 22(1). doi: 10.1515/sagmb-2023-0017. PMID: 37592851. biorXiv

[34] W. Li, A. Prasad, N. Miolane, K. Dao Duc (2023), “Using a Riemannian elastic metric for statistical analysis of tumor cell shape heterogeneity”, In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2023. Lecture Notes in Computer Science, vol 14072. Springer, Cham. https://doi.org/10.1007/978-3-031-38299-4_60 biorXiv

[33] F.E. Acosta, S., Sanborn, K., Dao Duc, M., Madhav, M., and N. Miolane (2023), “Quantifying Local Extrinsic Curvature in Neural Manifolds”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 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