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Netzwerkinferenz (Systembiologie) (German Wikipedia)

Analysis of information sources in references of the Wikipedia article "Netzwerkinferenz (Systembiologie)" in German language version.

refsWebsite
Global rank German rank
13doi.org
2nd place
3rd place
12nih.gov
4th place
7th place
1zdb-katalog.de
123rd place
6th place
1jstor.org
26th place
153rd place
1dreamchallenges.org
low place
low place

doi.org

  • M. Weber, S. G. Henkel, S. Vlaic, R. Guthke, E. J. van Zoelen, D. Driesch: Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0. In: BMC Systems Biology. Band 7, 2013, S. 1, doi:10.1186/1752-0509-7-1, PMID 23280066.
  • S. Vlaic, T. Conrad, C. Tokarski-Schnelle, M. Gustafsson, U. Dahmen, R. Guthke, S. Schuster: ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks. In: Scientific Reports. Band 8, Nr. 1, 2018, S. 433, doi:10.1038/s41598-017-18370-2, PMID 29323246.
  • S. Vlaic, W. Schmidt-Heck, M. Matz-Soja, E. Marbach, J. Linde, A. Meyer-Baese, S. Zellmer, R. Guthke, R. Gebhardt: The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes. In: BMC Systems Biology. Band 6, 2012, S. 147, doi:10.1186/1752-0509-6-147.
  • S. M. Colby, R. S. McClure, C. C. Overall et al.: Improving network inference algorithms using resampling methods. In: BMC Bioinformatics. Band 19, 2018, S. 376, doi:10.1186/s12859-018-2402-0.
  • J. Linde, P. Hortschansky, E. Fazius, A. Brakhage, R. Guthke, H. Haas: Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach. In: BMC Systems Biology. Band 6, 19. Januar 2012, S. 6, doi:10.1186/1752-0509-6-6.
  • Omid Abbaszadeh, Ali Reza Khanteymoori, Ali Azarpeyvand: Parallel Algorithms for Inferring Gene Regulatory Networks. A Review. In: Current Genomics. Band 19, S. 603–614, doi:10.2174/1389202919666180601081718.
  • E. P. van Someren, B. L. Vaes, W. T. Steegenga, A. M. Sijbers, K. J. Dechering, M. J. Reinders: Least absolute regression network analysis of the murine osteoblast differentiation network. In: Bioinformatics. Band 22, 2006, S. 477, doi:10.1093/bioinformatics/bti816, PMID 16332709.
  • B. Efron, T. Hastie, I. Johnstone, R. Tibshirani: Least angle regression. In: Annals of Statistics. Band 32, 2004, S. 409–499, doi:10.1214/009053604000000067.
  • N. Friedman, M. Linial, I. Nachman, D. Pe'er: Using bayesian networks to analyze expression data. In: Journal of Computational Biology. Band 7, 2000, S. 601–620, doi:10.1089/106652700750050961, PMID 11108481.
  • X. Liang, W. C. Young, L.H. Hung, A. E. Raftery, K.Y. Yeung: Integration of Multiple Data Sources for Gene Network Inference Using Genetic Perturbation Data. In: Journal of Computational Biology. Band 26, Nr. 10, 2019, S. 1113‐1129, doi:10.1089/cmb.2019.0036.
  • A. Wille, P. Zimmermann, E. Vranová, A. Fürholz, O. Laule, S. Bleuler, L. Hennig, A. Prelic, P. von Rohr, L. Thiele, E. Zitzler, W. Gruissem, P. Bühlmann: Sparse graphical gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. In: Genome Biology. Band 5, Nr. 11, 2004, S. R92, doi:10.1186/gb-2004-5-11-r92, PMID 15535868.
  • K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-Favera, A. Califano: Reverse engineering of regulatory networks in human B cells. In: Nature Genetics. Band 37, 2005, S. 382–390, doi:10.1038/ng1532, PMID 15778709.
  • D. Marbach, J. C. Costello, R. Küffner, N. M. Vega, R. J. Prill, D.M. Camacho et al.: Wisdom of crowds for robust gene network inference. In: Nature Methods. Band 9, 2012, S. 796–804, doi:10.1038/nmeth.2016, PMID 22796662.

dreamchallenges.org

jstor.org

  • R. Tibshirani: Regression shrinkage and selection via the Lasso. In: Journal of the Royal Statistical Society, Series B. Band 58, 1996, S. 267–288, JSTOR:2346178.

nih.gov

ncbi.nlm.nih.gov

  • M. Weber, S. G. Henkel, S. Vlaic, R. Guthke, E. J. van Zoelen, D. Driesch: Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0. In: BMC Systems Biology. Band 7, 2013, S. 1, doi:10.1186/1752-0509-7-1, PMID 23280066.
  • J. Linde, S. Schulze, S. G. Henkel, R. Guthke: Data- and knowledge-based modeling of gene regulatory networks. An update. In: EXCLI Journal. Band 14, 2015, ISSN 1611-2156, S. 346–378, PMID 27047314.
  • S. Vlaic, T. Conrad, C. Tokarski-Schnelle, M. Gustafsson, U. Dahmen, R. Guthke, S. Schuster: ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks. In: Scientific Reports. Band 8, Nr. 1, 2018, S. 433, doi:10.1038/s41598-017-18370-2, PMID 29323246.
  • R. Guthke, U. Möller, M. Hoffmann, F. Thies, S. Töpfer: Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. In: Bioinformatics. Band 21, 2005, S. 1626–1634, PMID 15613398.
  • S. Liang, S. Fuhrman, R. Somogyi: Reveal, a general reverse engineering algorithm for inference of genetic network architectur. In: Pacific Symposium on Biocomputing. Band 1998, 1998, S. 18–29, PMID 9697168.
  • E. P. van Someren, B. L. Vaes, W. T. Steegenga, A. M. Sijbers, K. J. Dechering, M. J. Reinders: Least absolute regression network analysis of the murine osteoblast differentiation network. In: Bioinformatics. Band 22, 2006, S. 477, doi:10.1093/bioinformatics/bti816, PMID 16332709.
  • R. Bonneau, D.J. Reiss, P. Shannon, M. Facciotti, L. Hood, N. S. Baliga et al.: The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. In: Genome Biology. Band 7, Nr. 5, 2006, S. R36, PMID 16686963.
  • N. Friedman, M. Linial, I. Nachman, D. Pe'er: Using bayesian networks to analyze expression data. In: Journal of Computational Biology. Band 7, 2000, S. 601–620, doi:10.1089/106652700750050961, PMID 11108481.
  • W. C. Young, A. E. Raftery, K. Y. Yeung: Fast Bayesian inference for gene regulatory networks using ScanBMA. In: BMC Systems Biology. Band 8, 2014, S. 47, PMID 24742092.
  • A. Wille, P. Zimmermann, E. Vranová, A. Fürholz, O. Laule, S. Bleuler, L. Hennig, A. Prelic, P. von Rohr, L. Thiele, E. Zitzler, W. Gruissem, P. Bühlmann: Sparse graphical gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. In: Genome Biology. Band 5, Nr. 11, 2004, S. R92, doi:10.1186/gb-2004-5-11-r92, PMID 15535868.
  • K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-Favera, A. Califano: Reverse engineering of regulatory networks in human B cells. In: Nature Genetics. Band 37, 2005, S. 382–390, doi:10.1038/ng1532, PMID 15778709.
  • D. Marbach, J. C. Costello, R. Küffner, N. M. Vega, R. J. Prill, D.M. Camacho et al.: Wisdom of crowds for robust gene network inference. In: Nature Methods. Band 9, 2012, S. 796–804, doi:10.1038/nmeth.2016, PMID 22796662.

zdb-katalog.de

  • J. Linde, S. Schulze, S. G. Henkel, R. Guthke: Data- and knowledge-based modeling of gene regulatory networks. An update. In: EXCLI Journal. Band 14, 2015, ISSN 1611-2156, S. 346–378, PMID 27047314.