Li Chen

Summary

Affiliation: Virginia Polytechnic Institute and State University
Country: USA

Publications

  1. ncbi Knowledge-guided multi-scale independent component analysis for biomarker identification
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Bioinformatics 9:416. 2008
  2. ncbi Biomarker identification by knowledge-driven multilevel ICA and motif analysis
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Int J Data Min Bioinform 3:365-81. 2009
  3. ncbi Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Int J Comput Biol Drug Des 2:1-20. 2009
  4. ncbi Motif-guided sparse decomposition of gene expression data for regulatory module identification
    Ting Gong
    Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA
    BMC Bioinformatics 12:82. 2011
  5. ncbi Multilevel support vector regression analysis to identify condition-specific regulatory networks
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Bioinformatics 26:1416-22. 2010
  6. ncbi Tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors
    Li Chen
    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    IEEE Trans Med Imaging 30:2044-58. 2011
  7. ncbi Identifying cancer biomarkers by network-constrained support vector machines
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Syst Biol 5:161. 2011
  8. ncbi Motif-directed network component analysis for regulatory network inference
    Chen Wang
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Bioinformatics 9:S21. 2008
  9. ncbi Identifying protein interaction subnetworks by a bagging Markov random field-based method
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Nucleic Acids Res 41:e42. 2013
  10. ncbi Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic
    Jinghua Gu
    Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA
    Bioinformatics 28:1990-7. 2012

Detail Information

Publications13

  1. ncbi Knowledge-guided multi-scale independent component analysis for biomarker identification
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Bioinformatics 9:416. 2008
    ..In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data...
  2. ncbi Biomarker identification by knowledge-driven multilevel ICA and motif analysis
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Int J Data Min Bioinform 3:365-81. 2009
    ....
  3. ncbi Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Int J Comput Biol Drug Des 2:1-20. 2009
    ..The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer...
  4. ncbi Motif-guided sparse decomposition of gene expression data for regulatory module identification
    Ting Gong
    Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA
    BMC Bioinformatics 12:82. 2011
    ..However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated...
  5. ncbi Multilevel support vector regression analysis to identify condition-specific regulatory networks
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Bioinformatics 26:1416-22. 2010
    ..New strategies are needed for reliable regulatory module identification...
  6. ncbi Tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors
    Li Chen
    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    IEEE Trans Med Imaging 30:2044-58. 2011
    ..This method combines the advantages of multivariate clustering, convex geometry analysis, and compartmental modeling approaches. The open-source MATLAB software of CAM-CM is publicly available from the Web...
  7. ncbi Identifying cancer biomarkers by network-constrained support vector machines
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Syst Biol 5:161. 2011
    ..Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers...
  8. ncbi Motif-directed network component analysis for regulatory network inference
    Chen Wang
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Bioinformatics 9:S21. 2008
    ..We propose a new approach, motif-directed NCA (mNCA), to integrate motif information and gene expression data to infer regulatory networks...
  9. ncbi Identifying protein interaction subnetworks by a bagging Markov random field-based method
    Li Chen
    Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
    Nucleic Acids Res 41:e42. 2013
    ....
  10. ncbi Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic
    Jinghua Gu
    Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA
    Bioinformatics 28:1990-7. 2012
    ..More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context...
  11. ncbi Defining NOTCH3 target genes in ovarian cancer
    Xu Chen
    Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, USA
    Cancer Res 72:2294-303. 2012
    ..Our findings define direct target genes of NOTCH3 and highlight the role of DLGAP5 in mediating the function of NOTCH3...
  12. ncbi CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues
    Li Chen
    Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA
    Bioinformatics 27:2607-9. 2011
    ..CAM-CM can dissect complex tissues into regions with differential tracer kinetics at pixel-wise resolution and provide a systems biology tool for defining imaging signatures predictive of phenotypes...
  13. ncbi Comparative analysis of methods for detecting interacting loci
    Li Chen
    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
    BMC Genomics 12:344. 2011
    ..Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted...