More publications can be found on Google Scholar

Selected publications

  1. Joining and splitting models with markov melding
    Goudie, Robert JB; Presanis, Anne M; Lunn, David; De Angelis, Daniela; Wernisch, Lorenz;
  2. Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.
    Penfold, Christopher A; Sybirna, Anastasiya; Reid, John E; Huang, Yun; Wernisch, Lorenz; Ghahramani, Zoubin; Grant, Murray; Surani, M Azim;
    Bioinformatics 2018, 34, i1005-i1013. 10.1093/bioinformatics/bty603
  3. GPseudoRank: a permutation sampler for single cell orderings.
    Strauß, Magdalena E; Reid, John E; Wernisch, Lorenz;
    Bioinformatics 2018, 10.1093/bioinformatics/bty664
  4. A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.
    Ng, Felicia S L; Ruau, David; Wernisch, Lorenz; Göttgens, Berthold;
    Brief. Bioinformatics 2018, 19, 162-173. 10.1093/bib/bbw102
  5. Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.
    Gabasova, Evelina; Reid, John; Wernisch, Lorenz;
    PLoS Comput. Biol. 2017, 13, e1005781. 10.1371/journal.pcbi.1005781
  6. A comparison of machine learning and Bayesian modelling for molecular serotyping.
    Newton, Richard; Wernisch, Lorenz;
    BMC Genomics 2017, 18, 606. 10.1186/s12864-017-3998-6
  7. Pseudotime estimation: deconfounding single cell time series.
    Reid, John E; Wernisch, Lorenz;
    Bioinformatics 2016, 32, 2973-80. 10.1093/bioinformatics/btw372
  8. An optimal stratified Simon two-stage design.
    Parashar, Deepak; Bowden, Jack; Starr, Colin; Wernisch, Lorenz; Mander, Adrian;
    Pharm Stat 2016, 15, 333-40. 10.1002/pst.1742
  9. Investigating inter-chromosomal regulatory relationships through a comprehensive meta-analysis of matched copy number and transcriptomics data sets.
    Newton, Richard; Wernisch, Lorenz;
    BMC Genomics 2015, 16, 967. 10.1186/s12864-015-2100-5
  10. A meta-analysis of multiple matched copy number and transcriptomics data sets for inferring gene regulatory relationships.
    Newton, Richard; Wernisch, Lorenz;
    PLoS ONE 2014, 9, e105522. 10.1371/journal.pone.0105522
  11. Key regulators control distinct transcriptional programmes in blood progenitor and mast cells.
    Calero-Nieto, Fernando J; Ng, Felicia S; Wilson, Nicola K; Hannah, Rebecca; Moignard, Victoria; Leal-Cervantes, Ana I; Jimenez-Madrid, Isabel; Diamanti, Evangelia; Wernisch, Lorenz; Göttgens, Berthold;
    EMBO J. 2014, 33, 1212-26. 10.1002/embj.201386825
  12. STEME: a robust, accurate motif finder for large data sets.
    Reid, John E; Wernisch, Lorenz;
    PLoS ONE 2014, 9, e90735. 10.1371/journal.pone.0090735
  13. Two novel pathway analysis methods based on a hierarchical model.
    Evangelou, Marina; Dudbridge, Frank; Wernisch, Lorenz;
    Bioinformatics 2014, 30, 690-7. 10.1093/bioinformatics/btt583
  14. Testing the utility of an integrated analysis of copy number and transcriptomics datasets for inferring gene regulatory relationships.
    Goh, Xin Yi; Newton, Richard; Wernisch, Lorenz; Fitzgerald, Rebecca;
    PLoS ONE 2013, 8, e63780. 10.1371/journal.pone.0063780
  15. Transcription factor and chromatin features predict genes associated with eQTLs.
    Wang, Dennis; Rendon, Augusto; Wernisch, Lorenz;
    Nucleic Acids Res. 2013, 41, 1450-63. 10.1093/nar/gks1339
  16. Cell signalling regulates dynamics of Nanog distribution in embryonic stem cell populations.
    Luo, Yang; Lim, Chea Lu; Nichols, Jennifer; Martinez-Arias, Alfonso; Wernisch, Lorenz;
    J R Soc Interface 2013, 10, 20120525. 10.1098/rsif.2012.0525
  17. Comparison of methods for competitive tests of pathway analysis.
    Evangelou, Marina; Rendon, Augusto; Ouwehand, Willem H; Wernisch, Lorenz; Dudbridge, Frank;
    PLoS ONE 2012, 7, e41018. 10.1371/journal.pone.0041018
  18. Transcription factor co-localization patterns affect human cell type-specific gene expression.
    Wang, Dennis; Rendon, Augusto; Ouwehand, Willem; Wernisch, Lorenz;
    BMC Genomics 2012, 13, 263. 10.1186/1471-2164-13-263
  19. STEME: efficient EM to find motifs in large data sets.
    Reid, John E; Wernisch, Lorenz;
    Nucleic Acids Res. 2011, 39, e126. 10.1093/nar/gkr574
  20. Empirical Bayesian models for analysing molecular serotyping microarrays.
    Newton, Richard; Hinds, Jason; Wernisch, Lorenz;
    BMC Bioinformatics 2011, 12, 88. 10.1186/1471-2105-12-88
  21. An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis.
    Zhang, Yi; Hatch, Kim A; Bacon, Joanna; Wernisch, Lorenz;
    BMC Syst Biol 2010, 4, 37. 10.1186/1752-0509-4-37
  22. Statistical model comparison applied to common network motifs.
    Domedel-Puig, Núria; Pournara, Iosifina; Wernisch, Lorenz;
    BMC Syst Biol 2010, 4, 18. 10.1186/1752-0509-4-18
  23. Variable structure motifs for transcription factor binding sites.
    Reid, John E; Evans, Kenneth J; Dyer, Nigel; Wernisch, Lorenz; Ott, Sascha;
    BMC Genomics 2010, 11, 30. 10.1186/1471-2164-11-30
  24. Transcriptional programs: modelling higher order structure in transcriptional control.
    Reid, John E; Ott, Sascha; Wernisch, Lorenz;
    BMC Bioinformatics 2009, 10, 218. 10.1186/1471-2105-10-218
  25. Estimating translational selection in eukaryotic genomes.
    Reis, Mario; Wernisch, Lorenz;
    Mol. Biol. Evol. 2009, 26, 451-61. 10.1093/molbev/msn272
  26. Using temporal correlation in factor analysis for reconstructing transcription factor activities.
    Pournara, Iosifina; Wernisch, Lorenz;
    EURASIP J Bioinform Syst Biol 2008, 172840. 10.1155/2008/172840
  27. A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis.
    Zhang, Yi; Hatch, Kim A; Wernisch, Lorenz; Bacon, Joanna;
    BMC Genomics 2008, 9, 87. 10.1186/1471-2164-9-87
  28. A comparative study of S/MAR prediction tools.
    Evans, Kenneth; Ott, Sascha; Hansen, Annika; Koentges, Georgy; Wernisch, Lorenz;
    BMC Bioinformatics 2007, 8, 71. 10.1186/1471-2105-8-71
  29. Factor analysis for gene regulatory networks and transcription factor activity profiles.
    Pournara, Iosifina; Wernisch, Lorenz;
    BMC Bioinformatics 2007, 8, 61. 10.1186/1471-2105-8-61
  30. A Hidden Markov model web application for analysing bacterial genomotyping DNA microarray experiments.
    Newton, Richard; Hinds, Jason; Wernisch, Lorenz;
    Appl. Bioinformatics 2006, 5, 211-8.
  31. Archaeology and evolution of transfer RNA genes in the Escherichia coli genome.
    Withers, Mike; Wernisch, Lorenz; Reis, Mario;
    RNA 2006, 12, 933-42. 10.1261/rna.2272306
  32. Applying GIFT, a Gene Interactions Finder in Text, to fly literature.
    Domedel-Puig, Núria; Wernisch, Lorenz;
    Bioinformatics 2005, 21, 3582-3. 10.1093/bioinformatics/bti578
  33. A universally applicable method of operon map prediction on minimally annotated genomes using conserved genomic context.
    Edwards, Martin T; Rison, Stuart C G; Stoker, Neil G; Wernisch, Lorenz;
    Nucleic Acids Res. 2005, 33, 3253-62. 10.1093/nar/gki634
  34. Solving the riddle of codon usage preferences: a test for translational selection.
    Reis, Mario; Savva, Renos; Wernisch, Lorenz;
    Nucleic Acids Res. 2004, 32, 5036-44. 10.1093/nar/gkh834
  35. Reconstruction of gene networks using Bayesian learning and manipulation experiments.
    Pournara, Iosifina; Wernisch, Lorenz;
    Bioinformatics 2004, 20, 2934-42. 10.1093/bioinformatics/bth337
  36. Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome.
    Reis, Mario; Wernisch, Lorenz; Savva, Renos;
    Nucleic Acids Res. 2003, 31, 6976-85.
  37. Identifying structural domains in proteins.
    Wernisch, Lorenz; Wodak, Shoshana J;
    Methods Biochem Anal 2003, 44, 365-85.
  38. Analysis of whole-genome microarray replicates using mixed models.
    Wernisch, Lorenz; Kendall, Sharon L; Soneji, Shamit; Wietzorrek, Andreas; Parish, Tanya; Hinds, Jason; Butcher, Philip D; Stoker, Neil G;
    Bioinformatics 2003, 19, 53-61.
  39. Folding free energy function selects native-like protein sequences in the core but not on the surface.
    Jaramillo, Alfonso; Wernisch, Lorenz; Héry, Stéphanie; Wodak, Shoshana J;
    Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 13554-9. 10.1073/pnas.212068599
  40. Automatic protein design with all atom force-fields by exact and heuristic optimization
    Wernisch, Lorenz; Hery, Stéphanie; Wodak, Shoshana J;
    Journal of molecular biology 2000, 301, 713-736.
  41. Identification of structural domains in proteins by a graph heuristic
    Wernisch, Lorenz; Hunting, Marcel; Wodak, Shoshana J;
    Proteins: Structure, Function, and Bioinformatics 1999, 35, 338-352.
  42. Trapezoid graphs and generalizations, geometry and algorithms
    Felsner, Stefan; Müller, Rudolf; Wernisch, Lorenz;
    Discrete Applied Mathematics 1997, 74, 13-32.
  43. Discrepancy and approximations for bounded vc-dimension
    Matoušek, Jiří; Welzl, Emo; Wernisch, Lorenz;
    Combinatorica 1993, 13, 455-466.
  44. Maximum k-chains in planar point sets: Combinatorial structure and algorithms
    Felsner, Stefan; Wernisch, Lorenz;
    SIAM Journal on Computing 1998, 28, 192-209.
  45. Trapezoid graphs and generalizations, geometry and algorithms
    Felsner, Stefan; Müller, Rudolf; Wernisch, Lorenz;
    Scandinavian workshop on algorithm theory 1994, 143-154.
  46. Markov chains for linear extensions, the two-dimensional case
    Felsner, Stefan; Wernisch, Lorenz;
    SODA 1997, 239-247.