@article{HakeBodenbergerGroth2023,
  author    = {Hake, Tim and Bodenberger, Bernhard and Groth, Detlef},
  title     = {In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks},
  series = {Human biology and public health},
  volume    = {1},
  journal   = {Human biology and public health},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {2748-9957},
  doi       = {10.52905/hbph2023.1.63},
  pages     = {16},
  year      = {2023},
  abstract  = {The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.},
  language  = {en}
}
@article{ViTrostLangeetal.2013,
  author    = {Vi, Son Lang and Trost, Gerda and Lange, Peggy and Czesnick, Hj{\"o}rdis and Rao, Nishta and Lieber, Diana and Laux, Thomas and Gray, William M. and Manley, James L. and Groth, Detlef and Kappel, Christian and Lenhard, Michael},
  title     = {Target specificity among canonical nuclear poly(A) polymerases in plants modulates organ growth and pathogen response},
  series = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA},
  volume    = {110},
  journal   = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA},
  number    = {34},
  publisher = {NATL ACAD SCIENCES},
  address   = {WASHINGTON},
  issn      = {0027-8424},
  doi       = {10.1073/pnas.1303967110},
  pages     = {13994 -- 13999},
  year      = {2013},
  abstract  = {Polyadenylation of pre-mRNAs is critical for efficient nuclear export, stability, and translation of the mature mRNAs, and thus for gene expression. The bulk of pre-mRNAs are processed by canonical nuclear poly(A) polymerase (PAPS). Both vertebrate and higher-plant genomes encode more than one isoform of this enzyme, and these are coexpressed in different tissues. However, in neither case is it known whether the isoforms fulfill different functions or polyadenylate distinct subsets of pre-mRNAs. Here we show that the three canonical nuclear PAPS isoforms in Arabidopsis are functionally specialized owing to their evolutionarily divergent C-terminal domains. A strong loss-of-function mutation in PAPS1 causes a male gametophytic defect, whereas a weak allele leads to reduced leaf growth that results in part from a constitutive pathogen response. By contrast, plants lacking both PAPS2 and PAPS4 function are viable with wild-type leaf growth. Polyadenylation of SMALL AUXIN UP RNA (SAUR) mRNAs depends specifically on PAPS1 function. The resulting reduction in SAUR activity in paps1 mutants contributes to their reduced leaf growth, providing a causal link between polyadenylation of specific pre-mRNAs by a particular PAPS isoform and plant growth. This suggests the existence of an additional layer of regulation in plant and possibly vertebrate gene expression, whereby the relative activities of canonical nuclear PAPS isoforms control de novo synthesized poly(A) tail length and hence expression of specific subsets of mRNAs.},
  language  = {en}
}
@article{HoangGryzikHoppeetal.2022,
  author    = {Hoang, Yen and Gryzik, Stefanie and Hoppe, Ines and Rybak, Alexander and Sch{\"a}dlich, Martin and Kadner, Isabelle and Walther, Dirk and Vera, Julio and Radbruch, Andreas and Groth, Detlef and Baumgart, Sabine and Baumgrass, Ria},
  title     = {PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments},
  series = {Frontiers in immunology},
  volume    = {13},
  journal   = {Frontiers in immunology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-3224},
  doi       = {10.3389/fimmu.2022.849329},
  pages     = {9},
  year      = {2022},
  abstract  = {Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4(+)T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.},
  language  = {en}
}
@article{KozielSchefflerTutkuvieneetal.2018,
  author    = {Koziel, Slawomir and Scheffler, Christiane and Tutkuviene, Janina and Jakimaviciene, Egle Marija and Mumm, Rebekka and Barbieri, Davide and Godina, Elena and El-Shabrawi, Mortada and Elhusseini, Mona and Musalek, Martin and Pruszkowska-Przybylska, Paulina and El Dash, Hanaa H. and Safar, Hebatalla Hassan and Lehmann, Andreas and Swanson, James and Bogin, Barry and Liu, Yuk-Chien and Groth, Detlef and Kirchengast, Sylvia and Siniarska, Anna and Nieczuja-Dwojacka, Joanna and Kralik, Miroslav and Satake, Takashi and Harc, Tomasz and Roelants, Mathieu and Hermanussen, Michael},
  title     = {Meeting Report: Growth and social environment},
  series = {Pediatric Endocrinology Reviews},
  volume    = {15},
  journal   = {Pediatric Endocrinology Reviews},
  number    = {4},
  publisher = {Medical Media},
  address   = {Netanya},
  issn      = {1565-4753},
  doi       = {10.17458/per.vol15.2018.ksh.mr.GrowthSocialEnvironment},
  pages     = {319 -- 329},
  year      = {2018},
  abstract  = {Twenty-two scientists met at Krobielowice, Poland, to discuss the impact of the social environment, spatial proximity, migration, poverty, but also psychological factors such as body perception and satisfaction, and social stressors such as elite sports, and teenage pregnancies, on child and adolescent growth. The data analysis included linear mixed effects models with different random effects, Monte Carlo analyses, and network simulations. The work stressed the importance of the peer group, but also included historic material, some considerations about body proportions, and growth in chronic liver, and congenital heart disease.},
  language  = {en}
}
@misc{HermanussenIpsenMummetal.2016,
  author    = {Hermanussen, Michael and Ipsen, Josefin and Mumm, Rebekka and Assmann, Christian and Quitmann, Julia and Gomula, Aleksandra and Lehmann, Andreas and Jasch, Isabelle and Tassenaar, Vincent and Bogin, Barry and Satake, Takashi and Scheffler, Christiane and Nunez, Javier and Godina, Elena and Hardeland, Ruediger and Boldsen, Jesper L. and El-Shabrawi, Mortada and Elhusseini, Mona and Barbu, Carmen Gabriela and Pop, Ralucca and Soederhaell, Jani and Merker, Andrea and Swanson, James and Groth, Detlef},
  title     = {Stunted Growth. Proceedings of the 23rd Aschauer Soiree, Held at Aschauhof, Germany, November 7th 2015},
  series = {Pediatric Endocrinology Reviews},
  volume    = {13},
  journal   = {Pediatric Endocrinology Reviews},
  publisher = {Medical Media},
  address   = {Netanya},
  issn      = {1565-4753},
  pages     = {756 -- 767},
  year      = {2016},
  abstract  = {Twenty-four scientists met at Aschauhof, Altenhof, Germany, to discuss the associations between child growth and development, and nutrition, health, environment and psychology. Meta-analyses of body height, height variability and household inequality, in historic and modern growth studies published since 1794, highlighting the enormously flexible patterns of child and adolescent height and weight increments throughout history which do not only depend on genetics, prenatal development, nutrition, health, and economic circumstances, but reflect social interactions. A Quality of Life in Short Stature Youth Questionnaire was presented to cross-culturally assess health-related quality of life in children. Changes of child body proportions in recent history, the relation between height and longevity in historic Dutch samples and also measures of body height in skeletal remains belonged to the topics of this meeting. Bayesian approaches and Monte Carlo simulations offer new statistical tools for the study of human growth.},
  language  = {en}
}