References in zbMATH (referenced in 19 articles )

Showing results 1 to 19 of 19.
Sorted by year (citations)

  1. Mardia, Kanti V.; Wiechers, Henrik; Eltzner, Benjamin; Huckemann, Stephan F.: Principal component analysis and clustering on manifolds (2022)
  2. He, Xinwei; Sun, Xiaoqiang; Shao, Yongzhao: Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient survival (2021)
  3. Costa, Lilia; Smith, James Q.; Nichols, Thomas: A group analysis using the multiregression dynamic models for fMRI networked time series (2019)
  4. Chi, Eric C.; Allen, Genevera I.; Baraniuk, Richard G.: Convex biclustering (2017)
  5. Li, Qin; Liu, Bo: Clustering using an improved krill herd algorithm (2017)
  6. Atkins, Jamin; Sharma, Davinder Pal: Visualization of babble-speech interactions using Andrews curves (2016) ioport
  7. Bergomi, Mattia G.; Baratè, Adriano; Di Fabio, Barbara: Towards a topological fingerprint of music (2016)
  8. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  9. Wang, Y. X. Rachel; Jiang, Keni; Feldman, Lewis J.; Bickel, Peter J.; Huang, Haiyan: Inferring gene-gene interactions and functional modules using sparse canonical correlation analysis (2015)
  10. Wang, Y. X. Rachel; Huang, Haiyan: Review on statistical methods for gene network reconstruction using expression data (2014)
  11. Yu, Hong; Liu, Zhanguo; Wang, Guoyin: An automatic method to determine the number of clusters using decision-theoretic rough set (2014)
  12. Hardin, Johanna; Garcia, Stephan Ramon; Golan, David: A method for generating realistic correlation matrices (2013)
  13. Pirim, Harun; Ekåioälu, Burak; Perkins, Andy D.; Yüceer, Çetin: Clustering of high throughput gene expression data (2012)
  14. Feher, Kristen; Whelan, James; Müller, Samuel: Assessing modularity using a random matrix theory approach (2011)
  15. Jarman, I. H.; Etchells, T. A.; Bacciu, D.; Garibaldi, J. M.; Ellis, I. O.; Lisboa, P. J. G.: Clustering of protein expression data: a benchmark of statistical and neural approaches (2011) ioport
  16. Ma, Shuangge; Shi, Mingyu; Li, Yang; Yi, Danhui; Shia, Ben-Chang: Incorporating gene co-expression network in identification of cancer prognosis markers (2010) ioport
  17. Choi, Dongseok; Sharma, Srilakshmi M.; Pasadhika, Sirichai; Kang, Zhixin; Harrington, Christina A.; Smith, Justine R.; Planck, Stephen R.; Rosenbaum, James T.: Application of biostatistics and bioinformatics tools to identify putative transcription factor-gene regulatory network of ankylosing spondylitis and sarcoidosis (2009)
  18. Park, P. J.; Manjourides, J.; Bonetti, M.; Pagano, M.: A permutation test for determining significance of clusters with applications to spatial and gene expression data (2009)
  19. Langfelder, Peter; Zhang, Bin; Horvath, Steve: Defining clusters from a hierarchical cluster tree: The dynamic tree cut package for R. (2008) ioport