Li Jiang

Summary

Affiliation: Danish Institute of Agricultural Sciences
Country: Denmark

Publications

  1. pmc A Bayesian variable selection procedure to rank overlapping gene sets
    Axel Skarman
    Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Tjele DK 8830, Denmark
    BMC Bioinformatics 13:73. 2012
  2. doi request reprint Gene prioritization for livestock diseases by data integration
    Li Jiang
    Dept of Molecular Biology and Genetics, Aarhus Univ, Blichers alle 20, PO Box 50, DK 8830 Tjele, Denmark
    Physiol Genomics 44:305-17. 2012
  3. pmc A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records
    Li Jiang
    Department of Molecular Biology and Genetics, Aarhus University, DK 8830 Tjele, Denmark
    BMC Bioinformatics 15:315. 2014
  4. pmc Analysis of the real EADGENE data set: multivariate approaches and post analysis (open access publication)
    Peter Sørensen
    University of Aarhus, Faculty of Agricultural Sciences, Dept of Genetics and Biotechnology, PO Box 50 DK 8830 Tjele, Denmark
    Genet Sel Evol 39:651-68. 2007
  5. pmc Gene expression profiling of liver from dairy cows treated intra-mammary with lipopolysaccharide
    Li Jiang
    Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK 8830 Tjele, Denmark
    BMC Genomics 9:443. 2008
  6. pmc Gene set analysis methods applied to chicken microarray expression data
    Axel Skarman
    Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, DK 8830 Tjele, Denmark
    BMC Proc 3:S8. 2009

Detail Information

Publications6

  1. pmc A Bayesian variable selection procedure to rank overlapping gene sets
    Axel Skarman
    Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Tjele DK 8830, Denmark
    BMC Bioinformatics 13:73. 2012
    ..We applied Bayesian variable selection to differential expression to prioritize the molecular and genetic pathways involved in the responses to Escherichia coli infection in Danish Holstein cows...
  2. doi request reprint Gene prioritization for livestock diseases by data integration
    Li Jiang
    Dept of Molecular Biology and Genetics, Aarhus Univ, Blichers alle 20, PO Box 50, DK 8830 Tjele, Denmark
    Physiol Genomics 44:305-17. 2012
    ..To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions, and biomedical text data have been integrated systematically for ranking candidate genes in any livestock species...
  3. pmc A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records
    Li Jiang
    Department of Molecular Biology and Genetics, Aarhus University, DK 8830 Tjele, Denmark
    BMC Bioinformatics 15:315. 2014
    ..Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization...
  4. pmc Analysis of the real EADGENE data set: multivariate approaches and post analysis (open access publication)
    Peter Sørensen
    University of Aarhus, Faculty of Agricultural Sciences, Dept of Genetics and Biotechnology, PO Box 50 DK 8830 Tjele, Denmark
    Genet Sel Evol 39:651-68. 2007
    ..The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed...
  5. pmc Gene expression profiling of liver from dairy cows treated intra-mammary with lipopolysaccharide
    Li Jiang
    Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK 8830 Tjele, Denmark
    BMC Genomics 9:443. 2008
    ..coli lipopolysaccharide (LPS) treatment...
  6. pmc Gene set analysis methods applied to chicken microarray expression data
    Axel Skarman
    Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, DK 8830 Tjele, Denmark
    BMC Proc 3:S8. 2009
    ..Methods for predicting the possible annotations for genes with unknown function from the expression data at hand could be useful, but our results indicate that careful validation of the predictions is needed...