Feng Luo

Summary

Affiliation: Clemson University
Country: USA

Publications

  1. ncbi request reprint Modular organization of protein interaction networks
    Feng Luo
    Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634 0974, USA
    Bioinformatics 23:207-14. 2007
  2. pmc Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory
    Feng Luo
    Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
    BMC Bioinformatics 8:299. 2007
  3. pmc Snapshot of iron response in Shewanella oneidensis by gene network reconstruction
    Yunfeng Yang
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    BMC Genomics 10:131. 2009
  4. ncbi request reprint Application of random matrix theory to microarray data for discovering functional gene modules
    Feng Luo
    Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, South Carolina 29634, USA
    Phys Rev E Stat Nonlin Soft Matter Phys 73:031924. 2006
  5. pmc Massive-scale gene co-expression network construction and robustness testing using random matrix theory
    Scott M Gibson
    Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina, USA
    PLoS ONE 8:e55871. 2013
  6. pmc Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions
    Bo Li
    School of Computing, Clemson University, Clemson, SC 29634, USA
    BMC Syst Biol 5:73. 2011
  7. doi request reprint Identifying differentially expressed genes in cancer patients using a non-parameter Ising model
    Xumeng Li
    School of Computing, Clemson University, Clemson, SC, USA
    Proteomics 11:3845-52. 2011
  8. ncbi request reprint A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles
    Feng Luo
    Department of Computer Science, University of Texas at Dallas, Richardson, TX 75252, USA
    Bioinformatics 20:2605-17. 2004
  9. pmc Conserved non-coding regulatory signatures in Arabidopsis co-expressed gene modules
    Jacob B Spangler
    Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, United States of America
    PLoS ONE 7:e45041. 2012
  10. pmc A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins
    Pengfei Xuan
    School of Computing, Clemson University, Clemson, SC 29634, USA
    Glycobiology 22:552-60. 2012

Collaborators

Detail Information

Publications19

  1. ncbi request reprint Modular organization of protein interaction networks
    Feng Luo
    Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634 0974, USA
    Bioinformatics 23:207-14. 2007
    ..Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems...
  2. pmc Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory
    Feng Luo
    Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
    BMC Bioinformatics 8:299. 2007
    ....
  3. pmc Snapshot of iron response in Shewanella oneidensis by gene network reconstruction
    Yunfeng Yang
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    BMC Genomics 10:131. 2009
    ..In this work, we integrate physiological, transcriptomics and genetic approaches to delineate the iron response of S. oneidensis...
  4. ncbi request reprint Application of random matrix theory to microarray data for discovering functional gene modules
    Feng Luo
    Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, South Carolina 29634, USA
    Phys Rev E Stat Nonlin Soft Matter Phys 73:031924. 2006
    ..This transition is directly related to the structural change of the gene expression network from a global network to a network of isolated modules...
  5. pmc Massive-scale gene co-expression network construction and robustness testing using random matrix theory
    Scott M Gibson
    Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina, USA
    PLoS ONE 8:e55871. 2013
    ..Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust...
  6. pmc Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions
    Bo Li
    School of Computing, Clemson University, Clemson, SC 29634, USA
    BMC Syst Biol 5:73. 2011
    ..Synthetic lethal genetic interactions among proteins have been widely used to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions is still unclear...
  7. doi request reprint Identifying differentially expressed genes in cancer patients using a non-parameter Ising model
    Xumeng Li
    School of Computing, Clemson University, Clemson, SC, USA
    Proteomics 11:3845-52. 2011
    ..Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model...
  8. ncbi request reprint A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles
    Feng Luo
    Department of Computer Science, University of Texas at Dallas, Richardson, TX 75252, USA
    Bioinformatics 20:2605-17. 2004
    ..g. fixed topology structure; mis-clustered data which cannot be reevaluated). In this paper, we introduce a new hierarchical clustering algorithm that overcomes some of these drawbacks...
  9. pmc Conserved non-coding regulatory signatures in Arabidopsis co-expressed gene modules
    Jacob B Spangler
    Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, United States of America
    PLoS ONE 7:e45041. 2012
    ..Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome...
  10. pmc A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins
    Pengfei Xuan
    School of Computing, Clemson University, Clemson, SC 29634, USA
    Glycobiology 22:552-60. 2012
    ..Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array...
  11. pmc Characterization of the Shewanella oneidensis Fur gene: roles in iron and acid tolerance response
    Yunfeng Yang
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    BMC Genomics 9:S11. 2008
    ..In many bacterial species, coordinate regulation of iron homeostasis depends on the protein product of a Fur gene. Fur also plays roles in virulence, acid tolerance, redox-stress responses, flagella chemotaxis and metabolic pathways...
  12. pmc Core and periphery structures in protein interaction networks
    Feng Luo
    School of Computing, Clemson University, Clemson, SC, USA
    BMC Bioinformatics 10:S8. 2009
    ..Characterizing the structural properties of protein interaction networks will help illuminate the organizational and functional relationships among elements in biological systems...
  13. pmc The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks
    Stephen P Ficklin
    Plant and Environmental Sciences, Clemson University, Clemson, South Carolina 29634, USA
    Plant Physiol 154:13-24. 2010
    ..Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes...
  14. pmc Predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using short polypeptide clusters
    Yuehua Zhang
    School of Computing, Clemson University, Clemson, SC 29634, USA
    Proteome Sci 10:S4. 2012
    ..Protein synthetic lethal genetic interactions are useful to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions remains unclear...
  15. doi request reprint Discrete surface Ricci flow
    Miao Jin
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11794 4400, USA
    IEEE Trans Vis Comput Graph 14:1030-43. 2008
    ..They have the potential for a wide range of applications in graphics, geometric modeling, and medical imaging. We demonstrate their practical values by global surface parameterizations...
  16. ncbi request reprint Adaptive evolution after gene duplication in alpha-KT x 14 subfamily from Buthus martensii Karsch
    Zhijian Cao
    State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, PR China
    IUBMB Life 57:513-21. 2005
    ....
  17. pmc Hemodynamic and metabolic changes induced by cocaine in anesthetized rat observed with multimodal functional MRI
    Karl F Schmidt
    Center for Comparative NeuroImaging, University of Massachusetts, Worcester, MA, USA
    Psychopharmacology (Berl) 185:479-86. 2006
    ....
  18. pmc Identification and characterization of CPS1 as a hyaluronic acid synthase contributing to the pathogenesis of Cryptococcus neoformans infection
    Ambrose Jong
    Division of Hematology Oncology, Children s Hospital Los Angeles, Los Angeles, CA 90027, USA
    Eukaryot Cell 6:1486-96. 2007
    ..Together, our results support that C. neoformans CPS1 encodes hyaluronic acid synthase and that its product, hyaluronic acid, plays a role as an adhesion molecule during the association of endothelial cells with yeast...
  19. doi request reprint Optimal surface parameterization using inverse curvature map
    Yong Liang Yang
    Department of Computer Science and Technology, Tsinghua University, Beijing, China
    IEEE Trans Vis Comput Graph 14:1054-66. 2008
    ..Comparisons are conducted with existing methods and using different energies. Novel parameterization applications are also introduced...