Preprints

• 2022, “Statistical curve models for inferring 3D chromatin architecture”, E.Tuzhilina, T.Hastie, M.Segal

• 2022, “Smooth multi-period forecasting with application to prediction of COVID-19 cases”, E.Tuzhilina, T.Hastie, R.Tibshirani

• 2021, “Weighted Low Rank Matrix Approximation and acceleration”, E.Tuzhilina, T.Hastie

Journal Papers

• 2022, “Principal Component Analysis”, M.Greenacre, P.Groenen, T.Hastie, A.D'Enza, A.Markos, E.Tuzhilina, accepted to Nature Reviews Methods Primers

• 2021, “An Open Repository of Real-Time COVID-19 Indicators”, A. Reinhart, L. Brooks, M. Jahja, A.Rumack, J.Tang, W. Saeed, T.Arnold, A.Basu, J.Bien, A.Cabrera, A.Chin, E.Chua, B.Clark, N.DeFries, J.Forlizzi, S.Gratzl, A.Green, G.Haff, R.Han, A.Hu, S.Hyun, A.Joshi, J.Kim, A.Kuznetsov, W.Motte-Kerr, K.Lee, Y.Lee, Z.Lipton, M.Liu, L.Mackey, K.Mazaitis, D.McDonald, B.Narasimhan, N.Oliveira, P.Patil, A.Perer, C.Politsch, S.Rajanala, D.Rucker, N.Shah, V.Shankar, J.Sharpnack, D.Shemetov, N.Simon, V.Srivastava, S.Tan, R.Tibshirani, E.Tuzhilina, A.Nortwick, V.Ventura, L.Wasserman, J.Weiss, K.Williams, R.Rosenfeld, R.Tibshirani, Proceedings of the National Academy of Sciences

• 2021, “Canonical Correlation Analysis in high dimensions with structured regularization”, E.Tuzhilina, L.Tozzi, T.Hastie, Statisticsl Modelling, SAGE

• 2021, “Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis”, L.Tozzi, E.Tuzhilina, M.Glasser, T.Hastie, L.Williams, NeuroImage, Vol. 237, pp. 118-137

• 2020, “Principal curve approaches for inferring 3D chromatin architecture”, E.Tuzhilina, T.Hastie, M.Segal, Biostatistics

• 2017, “Analyzing the Data Bank of Proteins Space Structures (PDB); A Geometrical Approach”, E.Vilkul, A.Ivanov, A.Mishchenko, F.Popelensky, A.Tuzhilin, K.Shaitan, Springer, Journal of mathematical Sciences, Vol. 225, number 4, pp. 555–564

• 2015, Addendum to the article “Critical analysis of amino acids and polypeptides geometry”, A.Ivanov, A.Mishchenko, A.Tuzhilin, Springer, Continuous and Distributed Systems: Theory and Applications, Vol. 2, pp. 29–74

• 2015, “A geometric approach to the analysis of the data bank of the three-dimensional structures of proteins (PDB)”, E.Vilkul, A.Ivanov, A.Mishchenko, F.Popelensky, A.Tuzhilin, K.Shaitan, Intuit, Pure and Applied Mathematics, Vol. 20, number 3, pp. 33-46

• 2015, “Conformations of swivel chain as a model of protein folding”, E.Vilkul, A.Ivanov, A.Tuzhilin, The Journal of Nanostructures, Mathematical physics and modeling, Vol. 13, number 2, pp. 25-42

• 2014, “Geometry of amino acids and polypeptides: the case of X-ray analysis”, E.Vilkul, A.Tuzhilin, The Journal of Nanostructures, Mathematical physics and modeling, Vol. 11, number 2, pp. 5-27

Patents

• 2019, “Data-driven automated selection of profiles of translation professionals for translation tasks”, A.Ukrainets, V.Gusakov, I.Smolnikov, E.Tuzhilina, patent number US20190065463

• 2018, “System and method of intellectual automatic selection of performers of translation”, A.Ukrainets, E.Tuzhilina, V.Gusakov, I.Smolnikov, patent number RU2667030