Gruber, K., Gauster, T., Laaha, G., Regner, P., and Schmidt, J.: Profitability and investment risk of Texan power system winterization, Nat Energy, 2022[DOI]

Laimighofer, J., Melcher, M., and Laaha, G.: Parsimonious statistical learning models for low-flow estimation, Hydrol. Earth Syst. Sci., 26, 129–148, 2022. [DOI]

Tintner, J., Spangl, B., Grabner, M., Helama, S., Timonen, M., Kirchhefer, A.J., Reinig, F., Nievergelt, D., Krąpiec, M., Smidt, E.; MD dating: molecular decay (MD) in pinewood as a dating method. Sci Rep 10, 11255 (2020). [DOI]

Tintner, J., Spangl, B., Reiter, F., Smidt, E., Grabner, M.; Infrared spectral characterization of the molecular wood decay in terms of age. WOOD SCI TECHNOL. 54(2), 2020. [DOI]

Karanitsch-Ackerl S., Mayer K., Gauster T., Laaha G., Holawe F., Wimmer R., Grabner M.: A 400-year reconstruction of spring–summer precipitation and summer low flow from regional tree-ring chronologies in North-Eastern Austria. Journal of Hydrology Vol. 577, 123986;2019. [DOI]

Klanert G, Fernandez DJ, Weinguny M, Eisenhut P, Bühler E, Melcher, M, Titus SA, Diendorfer AB, Gludovacz E, Jadhav V, Xiao S, Stern B, Lal M, Shiloach J, Borth N.: A cross-species whole genome siRNA screen in suspension-cultured Chinese hamster ovary cells identifies novel engineering targets. Sci Rep. 2019; 9(1):8689. [DOI]

Walch N, Scharl T, Felföldi E, Sauer DG, Melcher M, Leisch F, Dürauer A, Jungbauer A: Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time. Biotechnol J. 2019; 14(7):e1800521. [DOI]

Sauer DG, Melcher M, Mosor M, Walch N, Berkemeyer M, Scharl-Hirsch T, Leisch F, Jungbauer A, Dürauer A.: Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2. Biotechnol Bioeng. 116 (8), 1999-2009; 2019. [DOI]

Klanert G., Bydlinski N., Agu P., Diendorfer A.B., Hackl M., Hanscho M. Melcher M., Baumann M., Grillari J., Borth N.: Transient manipulation of the expression level of selected growth rate correlating microRNAs does not increase growth rate in CHO-K1 cells. Elsevier, Vol. 295, 63 -70; 2019. [DOI]

Schlögl M., Stütz, R., Laaha, G., Melcher, M.: A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset. Elsevier, Vol. 127, 134-149; 2019. [DOI]

Walter, T., Zink, R., Laaha, G., Zaller, J. G. & Heigl, F.: Fox sightings in a city are related to certain land use classes and sociodemographics: results from a citizen science project. BMC Ecology 18(1); 2018. [DOI]

Bhuyan-Erhardt, U., Erhardt, T. M., Laaha, G., Zang, C., Parajka, J. & Menzel, A.: Validation of drought indices using environmental indicators: streamflow and carbon flux data. Agricultural and Forest Meteorology;2019, 265, 218-226. [DOI]

Vcelar, S., Melcher, M., Auer, N., Hrdina, A., Puklowski, A., Leisch, F., Jadhav, V., Wenger, T., Baumann M. and Borth, N.: Changes in chromosome counts and patterns in CHO cell lines upon generation of recombinant cell lines and subcloning. Biotechnology Journal; 2018. [DOI]

Baumann, M; Vcelar, S; Hernandez-Lopez, I; Melcher, M; Borth, N.: Assessment of genomic rearrangements in Chinese Hamster Ovary (CHO) cells. New Biotechnology; 2016, 33: S3-S3. [DOI]

Vcelar, S.; Jadhav, V.; Melcher, M., Auer, N.; Hrdina, A.; Sagmeister, R.; Heffner, K.; Puklowski, A.; Betenbaugh, M.; Wenger, T.; Leisch, F.; Baumann, M. and Borth, N.: Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting. Biotechnology and Bioengineering;2017. [DOI]

Puschenreiter M.; Gruber B.; Wenzel W. W.; Schindlegger Y.; Hann S.; Spangl B.; Schenkeveld W. D.C.; Kraemer S. M.; Oburger E.: Phytosiderophore-induced mobilization and uptake of Cd, Cu, Fe, Ni, Pb and Zn by wheat plants grown on metal-enriched soils. Environmental and Experimental Botany 138, 67-76, 2017. [DOI/ PDF]

Melcher, M.; Scharl, T.; Luchner, M.; Striedner, G. and Leisch, F.: Boosted Structured Additive Regression for Escherichia coli Fed-Batch Fermentation Modeling. Biotechnology and Bioengineering 114 (2), 321-334, 2017. [DOI]

Brockhaus S.; Melcher M.; Leisch F. and Greven S.: Boosting flexible functional regression models with a high number of functional historical effects. Stat Comput (2017) 27:913-926 [bib / DOI]

Parajka, J.; Blaschke, A. P.; Blöschl, G.; Haslinger, K.; Hepp, G.; Laaha, G.; Schöner, W.; Trautvetter, H.; Viglione, A. and Zessner, M: Uncertainty contributions to low-flow projections in Austria. Hydrology and Earth System Sciences, 20(5), 2085-2101, doi:10.5194/hess-20-2085-2016, 2016. [DOI]

Van Lanen H.A.J.; Laaha G.; Kingston, D.; Gauster T. et al Hydrology needed to manage droughts: the 2015 European case. Hydrological Processes, 2016. [DOI / PDF]

Melcher M.; Scharl T.; Spangl B.; Luchner M.; Cserjan M.; Bayer K.; Leisch F.; Striedner G. The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations. Biotechnology Journal 10(11), 1770-1782, 2015. [bib / DOI]

Unbehaun W.; Uhlmann T.; Hössinger R.; Leisch F.; Gerike R. Women and men with care responsibilities in the European Alps - activity and mobility patterns of a diverse group. Mountain Research and Development, 34(3):276--290, 2014. [bib / DOI]

Van Loon A. F.; Laaha G. Hydrological drought severity explained by climate and catchment characteristics. Journal of Hydrology, doi:10.1016/j.jhydrol.2014.10.059, n.d. [DOI / PDF]

Haslinger K.; Koffler D.; Schöner W.; Laaha G. Exploring the link between meteorological drought and streamflow: Effects of climate-catchment interaction. Water Resources Research, 50(3), 2468–2487, doi:10.1002/2013WR015051, 2014. [DOI]

Laaha G.; Skøien J. O.; Blöschl G. Spatial prediction on river networks: comparison of top-kriging with regional regression. Hydrological Processes, 28(2), 315–324, doi:10.1002/hyp.9578, 2014. [DOI]

Skøien J. O.; Blöschl G.; Laaha G.; Pebesma E.; Parajka J.; Viglione A. rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences, 67, 180–190, doi:10.1016/j.cageo.2014.02.009, 2014. [DOI]

Szolgayova, E.; Laaha, G.; Blöschl, G.; Bucher, C. Factors influencing long range dependence in streamflow of European rivers: LONG RANGE DEPENDENCE IN RUNOFF OF EUROPEAN RIVERS. Hydrological Processes, 28(4), 1573–1586, doi:10.1002/hyp.9694, 2014. [DOI]

Nosrati K.; Laaha G.; Sharifnia S. A.; Rahimi M. Regional low flow analysis in Sefidrood Drainage Basin, Iran using principal component regression. Hydrology Research, doi:10.2166/nh.2014.087, n.d. [DOI / PDF]

Strohmeier S.; Laaha G.; Holzmann H.; Klik A. Magnitude and occurrence probability of soil loss: a risk analytical approach for the plot scale for two sites in Lower Austria. Land Degradation & Development, n.d. [DOI / PDF]

Dolnicar, S; Grun, B; Leisch, F; Schmidt, K. Required Sample Sizes for Data- Driven Market Segmentation Analyses in Tourism. Journal of Travel Research, 53(3):296--306, 2014. [bib / DOI]

Egger, B; Spangl, B; Koschier, EH. Habituation in Frankliniella occidentalis to deterrent plant compounds and their blend. ENTOMOL EXP APPL., 151(3): 231-238,2014. [DOI]

Eugster, MJA; Leisch, F; Strobl, C. (Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithm. Computational Statistics & Data Analysis, 71: 986-1000,2014. [bib / DOI / preprint]