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Hegele, L. A., Scagliarini, A., Sbragaglia, M., Mattila, K. K., Philippi, P. C., Puleri, D. F., ... Randles, A. (2018). High-Reynolds-number turbulent cavity flow using the lattice Boltzmann method. Physical Review E, 98(4), [043302]. https://doi.org/10.1103/PhysRevE.98.043302

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Alberucci, A., Laudyn, U. A., Piccardi, A., Kwasny, M., Klus, B., Karpierz, M. A., & Assanto, G. (2017). Nonlinear continuous-wave optical propagation in nematic liquid crystals: Interplay between reorientational and thermal effects. Physical Review E, 96(1), [012703]. https://doi.org/10.1103/PhysRevE.96.012703

Stupnikov, A., Tripathi, S., De Matos Simoes, R., McArt, D., Salto-Tellez, M., Glazko, G., ... Emmert-Streib, F. (2016). SamExploreR: Exploring reproducibility and robustness of RNA-seq results based on SAM files. Bioinformatics, 32(21), 3345-3347. https://doi.org/10.1093/bioinformatics/btw475

Palyulin, V. V., Chechkin, A. V., Klages, R., & Metzler, R. (2016). Search reliability and search efficiency of combined Lévy-Brownian motion: Long relocations mingled with thorough local exploration. Journal of Physics A: Mathematical and Theoretical, 49(39), [394002]. https://doi.org/10.1088/1751-8113/49/39/394002

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Häkkinen, A., & Ribeiro, A. S. (2016). Characterizing rate limiting steps in transcription from RNA production times in live cells. Bioinformatics, 32(9), 1346-1352. https://doi.org/10.1093/bioinformatics/btv744

Aho, V., Mattila, K., Kühn, T., Kekäläinen, P., Pulkkinen, O., Minussi, R. B., ... Timonen, J. (2016). Diffusion through thin membranes: Modeling across scales. Physical Review E, 93(4), [043309]. https://doi.org/10.1103/PhysRevE.93.043309

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Larjo, A., & Lähdesmäki, H. (2015). Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning. Eurasip Journal on Bioinformatics and Systems Biology, 2015(1), [6]. https://doi.org/10.1186/s13637-015-0024-7

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Mahmoudvand, R., Alehosseini, F., & Rodrigues, P. C. (2015). Forecasting mortality rate by singular spectrum analysis. REVSTAT STATISTICAL JOURNAL, 13(3), 193-206.

Safdari, H., Cherstvy, A. G., Chechkin, A. V., Thiel, F., Sokolov, I. M., & Metzler, R. (2015). Quantifying the non-ergodicity of scaled Brownian motion. Journal of Physics A: Mathematical and Theoretical, 48(37), [375002]. https://doi.org/10.1088/1751-8113/48/37/375002

Devassy, L., Jisha, C. P., Alberucci, A., & Kuriakose, V. C. (2015). Parity-time-symmetric solitons in trapped Bose-Einstein condensates and the influence of varying complex potentials: A variational approach. Physical Review E, 92(2), [022914]. https://doi.org/10.1103/PhysRevE.92.022914

Krüsemann, H., Godec, A., & Metzler, R. (2015). Ageing first passage time density in continuous time random walks and quenched energy landscapes. Journal of Physics A: Mathematical and Theoretical, 48(28), [285001]. https://doi.org/10.1088/1751-8113/48/28/285001

Godec, A., & Metzler, R. (2015). Signal focusing through active transport. Physical Review E, 92(1), [010701]. https://doi.org/10.1103/PhysRevE.92.010701

Rodrigues, P. C., Monteiro, A., & Lourenço, V. M. (2015). A robust AMMI model for the analysis of genotype-by-environment data. Bioinformatics, 32(1), 58-66. https://doi.org/10.1093/bioinformatics/btv533

Mattila, K. K., Hegele, L. A., & Philippi, P. C. (2015). Investigation of an entropic stabilizer for the lattice-Boltzmann method. Physical Review E, 91(6), [063010]. https://doi.org/10.1103/PhysRevE.91.063010

Godec, A., & Metzler, R. (2015). Optimization and universality of Brownian search in a basic model of quenched heterogeneous media. Physical Review E, 91(5), [052134]. https://doi.org/10.1103/PhysRevE.91.052134

Cherstvy, A. G., & Metzler, R. (2015). Ergodicity breaking, ageing, and confinement in generalized diffusion processes with position and time dependent diffusivity. Journal of Statistical Mechanics: Theory and Experiment, 2015(5), [P05010]. https://doi.org/10.1088/1742-5468/2015/05/P05010

Safdari, H., Chechkin, A. V., Jafari, G. R., & Metzler, R. (2015). Aging scaled Brownian motion. Physical Review E, 91(4), [042107]. https://doi.org/10.1103/PhysRevE.91.042107

Blavatska, V., & Metzler, R. (2015). Conformational properties of complex polymers: Rosette versus star-like structures. Journal of Physics A: Mathematical and Theoretical, 48(13), [135001]. https://doi.org/10.1088/1751-8113/48/13/135001

Subramaniyam, N. P., & Hyttinen, J. (2015). Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks. Physical Review E, 91(2), [022927]. https://doi.org/10.1103/PhysRevE.91.022927

Häkkinen, A., & Ribeiro, A. S. (2015). Estimation of GFP-tagged RNA numbers from temporal fluorescence intensity data. Bioinformatics, 31(1), 69-75. https://doi.org/10.1093/bioinformatics/btu592

Yang, Z., Peltonen, J., & Kaski, S. (2015). Majorization-minimization for manifold embedding. Journal of Machine Learning Research, 38, 1088-1097.

Tripathi, S., Dehmer, M., & Emmert-Streib, F. (2014). NetBioV: An R package for visualizing large network data in biology and medicine. Bioinformatics, 30(19), 2834-2836. https://doi.org/10.1093/bioinformatics/btu384

Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2014). Gene Sets Net Correlations Analysis (GSNCA): A multivariate differential coexpression test for gene sets. Bioinformatics, 30(3), 360-368. https://doi.org/10.1093/bioinformatics/btt687

Rodrigues, P. C., Moreira, E. E., Jesus, V. M., & Mexia, J. T. (2014). Structured orthogonal families of one and two strata prime basis factorial models. Statistical Papers, 55(3), 603-614. https://doi.org/10.1007/s00362-013-0507-0

Peltonen, J., & Lin, Z. (2013). Information retrieval perspective to meta-visualization. Journal of Machine Learning Research, 29, 165-180.

Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2012). Gene set analysis for self-contained tests: Complex null and specific alternative hypotheses. Bioinformatics, 28(23), 3073-3080. https://doi.org/10.1093/bioinformatics/bts579

Pereira, D. G., Rodrigues, P. C., Mejza, S., & Mexia, J. T. (2012). A comparison between joint regression analysis and the AMMI model: A case study with barley. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 82(2), 193-207. https://doi.org/10.1080/00949655.2011.615839

Wu, G. H. M., Auvinen, A., Yen, A. M. F., Hakama, M., Walter, S. D., & Chen, H. H. (2012). A stochastic model for survival of early prostate cancer with adjustments for leadtime, length bias, and over-detection. Biometrical Journal, 54(1), 20-44. https://doi.org/10.1002/bimj.201000107

Pearlman, A., Campbell, C., Brooks, E., Genshaft, A., Shajahan, S., Ittman, M., ... Ostrer, H. (2012). Clustering-based method for developing a genomic copy number alteration signature for predicting the metastatic potential of prostate cancer. JOURNAL OF PROBABILITY AND STATISTICS, [873570]. https://doi.org/10.1155/2012/873570

Assanto, G., Marchant, T. R., Minzoni, A. A., & Smyth, N. F. (2011). Reorientational versus Kerr dark and gray solitary waves using modulation theory. Physical Review E, 84(6), [066602]. https://doi.org/10.1103/PhysRevE.84.066602

Yu, G., Zhang, B., Bova, G. S., Xu, J., Shih, I. M., & Wang, Y. (2011). BACOM: In silico detection of genomic deletion types and correction of normal cell contamination in copy number data. Bioinformatics, 27(11), 1473-1480. [btr183]. https://doi.org/10.1093/bioinformatics/btr183

Potapov, I., Volkov, E., & Kuznetsov, A. (2011). Dynamics of coupled repressilators: The role of mRNA kinetics and transcription cooperativity. Physical Review E, 83(3), [031901]. https://doi.org/10.1103/PhysRevE.83.031901

Peltonen, J., & Kaski, S. (2011). Generative modeling for maximizing precision and recall in information visualization. Journal of Machine Learning Research, 15, 579-587.

Altay, G., & Emmert-Streib, F. (2010). Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics, 26(14), 1738-1744. [btq259]. https://doi.org/10.1093/bioinformatics/btq259

Emmert-Streib, F. (2010). Exploratory analysis of spatiotemporal patterns of cellular automata by clustering compressibility. Physical Review E, 81(2), [026103]. https://doi.org/10.1103/PhysRevE.81.026103

Glazko, G. V., & Emmert-Streib, F. (2009). Unite and conquer: Univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics, 25(18), 2348-2354. https://doi.org/10.1093/bioinformatics/btp406

Emmert-Streib, F., & Dehmer, M. (2009). Fault tolerance of information processing in gene networks. Physica A: Statistical Mechanics and Its Applications, 388(4), 541-548. https://doi.org/10.1016/j.physa.2008.10.032

Knuuti, M., & Länsivaara, T. (2019). Performance of Variable Partial Factor approach in a slope design. teoksessa 13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019 https://doi.org/10.22725/ICASP13.475