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121.
Kong, B.* et al.: Dynamic landscape of pancreatic carcinogenesis reveals early molecular networks of malignancy. Gut 67, 146-156 (2016)
122.
Kong, B.* et al.: Oncogenic Kras promotes early pancreatic carcinogenesis by perpetuating elements of natural inflammatory response. Pancreatol. 16, S. 11 (2016)
123.
Krause, L. et al.: A computational model to predict severity of atopic eczema from 30 serum proteins. J. Allergy Clin. Immunol. 138, 1207-1210.e2 (2016)
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Krumsiek, J. ; Bartel, J. & Theis, F.J.: Computational approaches for systems metabolomics. Curr. Opin. Biotechnol. 39, 198-206 (2016)
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Kuepper, M.K.* et al.: Characterization of multiple B cell subsets in peripheral blood of psoriasis patients identifies a correlation of regulatory B cells and disease severity. J. Invest. Dermatol. 136, S217-S217 (2016)
126.
Laimighofer, M. ; Krumsiek, J. ; Buettner, F. & Theis, F.J.: Unbiased prediction and feature selection in high-dimensional survival regression. J. Comput. Biol. 23, 279-290 (2016)
127.
Lauffer, F.* et al.: Interface dermatitis shows a distinctive molecular signature independent from individual disease background. J. Invest. Dermatol. 136, S219-S219 (2016)
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Lauffer, F.* et al.: A distinct molecular signature of interface dermatitis as determined by gene expression analysis combined with disease independent histological phenotyping. Exp. Dermatol. 25, 30-31 (2016)
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Luber, B.* et al.: Identification of predictive response and resistance factors to targeted therapy in gastric cancer using a systems medicine approach. Eur. J. Cancer 68, S135-S135 (2016)
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Miettinen, J.* et al.: Separation of uncorrelated stationary time series using autocovariance matrices. J. Time Ser. Anal. 37, 337-354 (2016)
131.
Much, D. et al.: Lactation is associated with altered metabolomic signatures in women with gestational diabetes. Diabetologia 59, 2193-2202 (2016)
132.
Much, D. et al.: Lactation is associated with altered metabolomic signatures in women with gestational diabetes. Diabetologia 59, S187-S187 (2016)
133.
Preusse, M. ; Theis, F.J. & Müller, N.S.: miTALOS v2: Analyzing tissue specific microRNA function. PLoS ONE 11:e0151771 (2016)
134.
Rolle-Kampczyk, U.E.* et al.: Metabolomics reveals effects of maternal smoking on endogenous metabolites from lipid metabolism in cord blood of newborns. Metabolomics 12:76 (2016)
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Sacher, A. & Theis, F.J.: Von lernfähigen Maschinen lernen. Laborjournal 7-8, 59-61 (2016)
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Sacher, A. & Theis, F.J.: Einzigartige Zellen: Computerbasierte Methoden verbessern die Einzelzell-Analyse. Laborpraxis, DOI: undefined (2016)
137.
Scheel, C. et al.: A force-sensitive organoid assay to quantify regenerative potential of single primary human mammary cells. Cancer Res. 76:P1-06-02 (2016)
138.
Stoecker, K.* ; Sass, S. ; Theis, F.J. ; Hauner, H.* & Pfaffl, M.W.*: Inhibition of fat cell differentiation in 3T3-L1 pre-adipocytes by all-trans retinoic acid: Integrative analysis of transcriptomic and phenotypic data. Biomol. Detect. Quantif. 11, 31-44 (2016)
139.
Stojcheva, N.* et al.: MicroRNA-138 promotes acquired alkylator resistance in glioblastoma by targeting the Bcl-2-interacting mediator BIM. Oncotarget 7, 12937-12950 (2016)
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Styczynski, M.P.* & Theis, F.J.: Systems biology - the intersection of experiments and computation, underpinning biotechnology. Curr. Opin. Biotechnol. 39, IV-VI (2016)