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Ylinen, A., Mäkinen, J., & Kouhia, R. (2016). Two models for hydraulic cylinders in flexible multibody simulations. teoksessa Computational Methods for Solids and Fluids: Multiscale Analysis, Probability Aspects and Model Reduction (Sivut 463-493). (Computational Methods in Applied Sciences; Vuosikerta 41). Springer. https://doi.org/10.1007/978-3-319-27996-1_17

Yin, Q., Wang, Z., Xia, C., Dehmer, M., Emmert-Streib, F., & Jin, Z. (2020). A novel epidemic model considering demographics and intercity commuting on complex dynamical networks. Applied Mathematics and Computation, 386, [125517]. https://doi.org/10.1016/j.amc.2020.125517

Yang, D., Qian, Y., Cai, D., Yan, S., Kämäräinen, J-K., & Chen, K. (2019). Visibility-Aware Part Coding for Vehicle Viewing Angle Estimation. teoksessa 9th International Conference on Information Science and Technology, ICIST 2019 (Sivut 65-70). IEEE. https://doi.org/10.1109/ICIST.2019.8836907

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Wan, P., Tu, J., Dehmer, M., Zhang, S., & Emmert-Streib, F. (2019). Graph entropy based on the number of spanning forests of c-cyclic graphs. Applied Mathematics and Computation, 363, [124616]. https://doi.org/10.1016/j.amc.2019.124616

Vuojamo, V., & Eriksson, S-L. (2017). Integral kernels for k-hypermonogenic functions. Complex Variables and Elliptic Equations, 62(9), 1-12. https://doi.org/10.1080/17476933.2016.1250402

Valkealahti, S., & Manninen, M. (1993). Melting of copper clusters. Computational Materials Science, 1(2), 123-134. https://doi.org/10.1016/0927-0256(93)90003-6

Uusitalo, M. A., Peltonen, J., & Ryhänen, T. (2011). Machine learning: How it can help nanocomputing. Journal of Computational and Theoretical Nanoscience, 8(8), 1347-1363. https://doi.org/10.1166/jctn.2011.1821

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

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

Stockrahm, A., Lahtinen, V., Kangas, J. J. J., & Kotiuga, P. R. (Hyväksytty/painossa). Cuts for 3-D magnetic scalar potentials: Visualizing unintuitive surfaces arising from trivial knots. Computers and Mathematics with Applications. https://doi.org/10.1016/j.camwa.2019.05.023

Singh, A. K., Ahonen, A., Ghabcheloo, R., & Mueller, A. (2020). Introducing Multi-Convexity in Path Constrained Trajectory Optimization for Mobile Manipulators. teoksessa European Control Conference 2020, ECC 2020 (Sivut 1178-1185). IEEE.

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

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

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

Orelma, H., & Vieira, N. (2017). Homogeneous (α,k)-Polynomial Solutions of the Fractional Riesz System in Hyperbolic Space. Complex Analysis and Operator Theory, 11(5), 1253–1267. https://doi.org/10.1007/s11785-017-0666-4

Mesaros, A., Diment, A., Elizalde, B., Heittola, T., Vincent, E., Raj, B., & Virtanen, T. (2019). Sound Event Detection in the DCASE 2017 Challenge. IEEE/ACM Transactions on Audio Speech and Language Processing, 27(6), 992-1006. https://doi.org/10.1109/TASLP.2019.2907016

Martins, L., Neeli-Venkata, R., Oliveira, S. M. D., Häkkinen, A., Ribeiro, A. S., & Fonseca, J. M. (2018). SCIP: a single-cell image processor toolbox. Bioinformatics, 34(24), 4318-4320. https://doi.org/10.1093/bioinformatics/bty505

Ma, L., & Ray, A. K. (2013). Growth behavior and magnetic properties of spherical uranium oxide nanoclusters. Journal of Computational and Theoretical Nanoscience, 10(2), 334-340. https://doi.org/10.1166/jctn.2013.2701

Ma, L., Wang, J., Hao, Y., & Wang, G. (2013). Density functional theory study of FePdn (n = 2-14) clusters and interactions with small molecules. Computational Materials Science, 68, 166-173. https://doi.org/10.1016/j.commatsci.2012.10.014

Ma, S., Ukkonen, L., Sydänheimo, L., & Björninen, T. (2019). Comparison of Human Head Phantoms with Different Complexities for Implantable Antenna Development. teoksessa 2018 International Applied Computational Electromagnetics Society (ACES) Symposium: 29 July-1 Aug. 2018, China IEEE. https://doi.org/10.23919/ACESS.2018.8669363

Luukko, P. J. J., Helske, J., & Räsänen, E. (2016). Introducing libeemd: a program package for performing the ensemble empirical mode decomposition. Computational Statistics, 31(2), 545-557. https://doi.org/10.1007/s00180-015-0603-9

Levämäki, H., Tian, L-Y., Vitos, L., & Ropo, M. (2019). An automated algorithm for reliable equation of state fitting of magnetic systems. Computational Materials Science, 156, 121-128. https://doi.org/10.1016/j.commatsci.2018.09.026

Kuva, J., Voutilainen, M., & Mattila, K. (2019). Modeling mass transfer in fracture flows with the time domain-random walk method. COMPUTATIONAL GEOSCIENCES. https://doi.org/10.1007/s10596-019-09852-5

Kuang, Y., Ma, S., Ukkonen, L., Virkki, J., & Björninen, T. (2019). Circularly Polarized Textile Tag Antenna for Wearable Passive UHF RFID Systems. teoksessa 2018 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2018 IEEE. https://doi.org/10.23919/ACESS.2018.8669314

Kartasalo, K., Latonen, L., Vihinen, J., Visakorpi, T., Nykter, M., & Ruusuvuori, P. (2018). Comparative analysis of tissue reconstruction algorithms for 3D histology. Bioinformatics, 34(17), 3013-3021. https://doi.org/10.1093/bioinformatics/bty210

Karilainen, T., Cramariuc, O., Kuisma, M., Tappura, K., & Hukka, T. I. (2015). Van der Waals interactions are critical in Car-Parrinello molecular dynamics simulations of porphyrin-fullerene dyads. Journal of Computational Chemistry, 36(9), 612-621. https://doi.org/10.1002/jcc.23834

Hella, L., Kuusisto, A., Meier, A., & Vollmer, H. (2019). Satisfiability of modal inclusion logic: Lax and strict semantics. ACM TRANSACTIONS ON COMPUTATIONAL LOGIC, 21(1), [7]. https://doi.org/10.1145/3356043

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

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

Guzmán Adán, A., Orelma, H., & Sommen, F. (2019). Hypermonogenic solutions and plane waves of the Dirac operator in Rp×Rq. Applied Mathematics and Computation, 346, 1-14. https://doi.org/10.1016/j.amc.2018.09.058

Gusrialdi, A., Xu, Y., Qu, Z., & Simaan, M. A. (2020). Resilient Cooperative Voltage Control for Distribution Network with High Penetration Distributed Energy Resources. teoksessa European Control Conference 2020, ECC 2020 (Sivut 1533-1539). IEEE.

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

Ghorbani, M., Dehmer, M., Maimani, H., Maddah, S., Roozbayani, M., & Emmert-Streib, F. (2020). The watching system as a generalization of identifying code. Applied Mathematics and Computation, 380, [125302]. https://doi.org/10.1016/j.amc.2020.125302

Eriksson, S-L., & Orelma, H. (2016). On k-Hypermonogenic Functions and Their Mean Value Properties. Complex Analysis and Operator Theory, 10(2), 311-325. https://doi.org/10.1007/s11785-015-0445-z

Eriksson, S. L., Orelma, H., & Vieira, N. (2018). Hypermonogenic Functions of Two Vector Variables. Complex Analysis and Operator Theory, 12(2), 555–570. https://doi.org/10.1007/s11785-017-0728-7

Enkavi, G., Li, J., Wen, P., Thangapandian, S., Moradi, M., Jiang, T., ... Tajkhorshid, E. (2014). A microscopic view of the mechanisms of active transport across the cellular membrane. Annual Reports in Computational Chemistry, 10, 77-125. https://doi.org/10.1016/B978-0-444-63378-1.00004-5

Emmert-Streib, F. (2012). Universal construction mechanism for networks from one-dimensional symbol sequences. Applied Mathematics and Computation, 219(3), 1020-1030. https://doi.org/10.1016/j.amc.2012.07.006

Emmert-Streib, F. (2012). Evolutionary dynamics of the spatial Prisoner's Dilemma with self-inhibition. Applied Mathematics and Computation, 218(11), 6482-6488. https://doi.org/10.1016/j.amc.2011.12.018

Emmert-Streib, F., & Dehmer, M. (2007). Topological mappings between graphs, trees and generalized trees. Applied Mathematics and Computation, 186(2), 1326-1333. https://doi.org/10.1016/j.amc.2006.07.162

Emmert-Streib, F., & Dehmer, M. (2007). Information theoretic measures of UHG graphs with low computational complexity. Applied Mathematics and Computation, 190(2), 1783-1794. https://doi.org/10.1016/j.amc.2007.02.095

Emmert-Streib, F. (2006). Algorithmic computation of knot polynomials of secondary structure elements of proteins. Journal of Computational Biology, 13(8), 1503-1512. https://doi.org/10.1089/cmb.2006.13.1503

Dumitrescu, B., Şicleru, B. C., & Avram, F. (2016). Modeling probability densities with sums of exponentials via polynomial approximation. Journal of Computational and Applied Mathematics, 292, 513–525. https://doi.org/10.1016/j.cam.2015.07.032

Dong, G., Shen, Y., He, H., Virkki, J., & Hu, S. (2017). Chipless graphene tag and dual-CP reader for Internet of Things. teoksessa 2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017 IEEE.

Dehmer, M., Emmert-Streib, F., & Shi, Y. (2015). Graph distance measures based on topological indices revisited. Applied Mathematics and Computation, 266, 623-633. https://doi.org/10.1016/j.amc.2015.05.072

Dehmer, M., Emmert-Streib, F., & Gesell, T. (2008). A comparative analysis of multidimensional features of objects resembling sets of graphs. Applied Mathematics and Computation, 196(1), 221-235. https://doi.org/10.1016/j.amc.2007.05.058

Dehmer, M., Grabner, M., Mowshowitz, A., & Emmert-Streib, F. (2013). An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants. Advances in Computational Mathematics, 39(2), 311-325. https://doi.org/10.1007/s10444-012-9281-0

Dehmer, M., & Emmert-Streib, F. (2007). Structural similarity of directed universal hierarchical graphs: A low computational complexity approach. Applied Mathematics and Computation, 194(1), 7-20. https://doi.org/10.1016/j.amc.2007.04.006

Dehmer, M., & Emmert-Streib, F. (2007). Comparing large graphs efficiently by margins of feature vectors. Applied Mathematics and Computation, 188(2), 1699-1710. https://doi.org/10.1016/j.amc.2006.11.185

Dehmer, M., Emmert-Streib, F., & Kilian, J. (2006). A similarity measure for graphs with low computational complexity. Applied Mathematics and Computation, 182(1), 447-459. https://doi.org/10.1016/j.amc.2006.04.006

Dehmer, M., Chen, Z., Shi, Y., Zhang, Y., Tripathi, S., Ghorbani, M., ... Emmert-Streib, F. (2019). On efficient network similarity measures. Applied Mathematics and Computation, 362, [124521]. https://doi.org/10.1016/j.amc.2019.06.035

Dehmer, M., Emmert-Streib, F., Mowshowitz, A., Ilić, A., Chen, Z., Yu, G., ... Tao, J. (2020). Relations and bounds for the zeros of graph polynomials using vertex orbits. Applied Mathematics and Computation, 380, [125239]. https://doi.org/10.1016/j.amc.2020.125239

Chen, Z., Dehmer, M., Emmert-Streib, F., & Shi, Y. (2014). Entropy bounds for dendrimers. Applied Mathematics and Computation, 242, 462-472. https://doi.org/10.1016/j.amc.2014.05.105

Carabias Orti, J. J., Nikunen, J., Virtanen, T., & Vera-Candeas, P. (2018). Multichannel Blind Sound Source Separation using Spatial Covariance Model with Level and Time Differences and Non-Negative Matrix Factorization. IEEE/ACM Transactions on Audio Speech and Language Processing, 26(9), 1512-1527. https://doi.org/10.1109/TASLP.2018.2830105

Belahcen, A., Kouhia, R., & Fonteyn, K. (2011). The different levels of magneto-mechanical coupling in energy conversion machines and devices. teoksessa Proceedings of the 4th International Conference on Computational Methods for Coupled Problems in Science and Engineering, COUPLED PROBLEMS 2011 (Sivut 472-483)

Batty, C., Paunonen, L., & Seifert, D. (2019). Optimal energy decay for the wave-heat system on a rectangular domain. SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 51(2), 808-819. https://doi.org/10.1137/18M1195796

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

Airiskallio, E., Nurmi, E., Väyrynen, I. J., Kokko, K., Ropo, M., Punkkinen, M. P. J., ... Vitos, L. (2014). Magnetic origin of the chemical balance in alloyed Fe-Cr stainless steels: First-principles and Ising model study. Computational Materials Science, 92, 135-140. https://doi.org/10.1016/j.commatsci.2014.05.036