Jieping Ye

Summary

Affiliation: Arizona State University
Country: USA

Publications

  1. doi request reprint Image-level and group-level models for Drosophila gene expression pattern annotation
    Qian Sun
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA
    BMC Bioinformatics 14:350. 2013
  2. pmc Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data
    Jieping Ye
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona, State University, Tempe, AZ, USA
    BMC Neurol 12:46. 2012
  3. pmc Multiple structure alignment and consensus identification for proteins
    Ivaylo Ilinkin
    Department of Computer Science, Gettysburg College, Gettysburg, PA, USA
    BMC Bioinformatics 11:71. 2010
  4. ncbi request reprint A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data
    Shuai Huang
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, PO Box 878809, Tempe, AZ 85287 8809, USA
    IEEE Trans Pattern Anal Mach Intell 35:1328-42. 2013
  5. pmc Learning sparse representations for fruit-fly gene expression pattern image annotation and retrieval
    Lei Yuan
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    BMC Bioinformatics 13:107. 2012
  6. pmc Automated annotation of Drosophila gene expression patterns using a controlled vocabulary
    Shuiwang Ji
    Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA
    Bioinformatics 24:1881-8. 2008
  7. pmc A bag-of-words approach for Drosophila gene expression pattern annotation
    Shuiwang Ji
    Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    BMC Bioinformatics 10:119. 2009
  8. doi request reprint Efficient methods for overlapping group lasso
    Lei Yuan
    Department of Computer Science and Engineering and the Center for Evolutionary Medicine and Informatics of the Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    IEEE Trans Pattern Anal Mach Intell 35:2104-16. 2013
  9. doi request reprint Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression
    Lei Yuan
    School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
    Bioinformatics 30:266-73. 2014
  10. pmc Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data
    Lei Yuan
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
    Neuroimage 61:622-32. 2012

Collaborators

Detail Information

Publications21

  1. doi request reprint Image-level and group-level models for Drosophila gene expression pattern annotation
    Qian Sun
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA
    BMC Bioinformatics 14:350. 2013
    ..It is thus imperative to design an automated system for efficient image annotation and comparison...
  2. pmc Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data
    Jieping Ye
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona, State University, Tempe, AZ, USA
    BMC Neurol 12:46. 2012
    ..Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD...
  3. pmc Multiple structure alignment and consensus identification for proteins
    Ivaylo Ilinkin
    Department of Computer Science, Gettysburg College, Gettysburg, PA, USA
    BMC Bioinformatics 11:71. 2010
    ..The algorithm is a heuristic in that it computes an approximation to the optimal alignment that minimizes the sum of the pairwise distances between the consensus and the transformed proteins...
  4. ncbi request reprint A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data
    Shuai Huang
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, PO Box 878809, Tempe, AZ 85287 8809, USA
    IEEE Trans Pattern Anal Mach Intell 35:1328-42. 2013
    ..We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research...
  5. pmc Learning sparse representations for fruit-fly gene expression pattern image annotation and retrieval
    Lei Yuan
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    BMC Bioinformatics 13:107. 2012
    ..However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords...
  6. pmc Automated annotation of Drosophila gene expression patterns using a controlled vocabulary
    Shuiwang Ji
    Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA
    Bioinformatics 24:1881-8. 2008
    ..However, the number of patterns generated by high-throughput in situ hybridization is rapidly increasing. It is, therefore, tempting to approach this problem by employing computational methods...
  7. pmc A bag-of-words approach for Drosophila gene expression pattern annotation
    Shuiwang Ji
    Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    BMC Bioinformatics 10:119. 2009
    ..Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task...
  8. doi request reprint Efficient methods for overlapping group lasso
    Lei Yuan
    Department of Computer Science and Engineering and the Center for Evolutionary Medicine and Informatics of the Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    IEEE Trans Pattern Anal Mach Intell 35:2104-16. 2013
    ..Experimental results show that the proposed algorithm is more efficient than existing state-of-the-art algorithms. Results also demonstrate the effectiveness of the nonconvex formulation for overlapping group Lasso...
  9. doi request reprint Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression
    Lei Yuan
    School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
    Bioinformatics 30:266-73. 2014
    ....
  10. pmc Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data
    Lei Yuan
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
    Neuroimage 61:622-32. 2012
    ..Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results...
  11. pmc Exploring spatial patterns of gene expression from fruit fly embryogenesis on the iPhone
    Sudhir Kumar
    Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University ASU, Tempe, AZ 85287 5301, USA
    Bioinformatics 28:2847-8. 2012
    ....
  12. doi request reprint Generalized linear discriminant analysis: a unified framework and efficient model selection
    Shuiwang Ji
    Department of Computer Science and Engineering, and the Center for Evolutionary Functional Genomics of The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    IEEE Trans Neural Netw 19:1768-82. 2008
    ..We conduct extensive experiments using a collection of high-dimensional data sets, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms...
  13. doi request reprint Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis
    Liang Sun
    Department of Computer Science and the Center for Evolutionary Medicine and Informatics CEMI of The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    IEEE Trans Pattern Anal Mach Intell 33:194-200. 2011
    ..We have conducted experiments using benchmark data sets. Experiments on multilabel data sets confirm the established equivalence relationships. Results also demonstrate the effectiveness and efficiency of the proposed CCA extensions...
  14. doi request reprint Modeling disease progression via multi-task learning
    Jiayu Zhou
    Center for Evolutionary Medicine and Informatics, The Biodesign Institute, ASU, Tempe, AZ 85287, USA
    Neuroimage 78:233-48. 2013
    ..The lack of predictable MRI biomarkers in later stages may contribute to the lower prediction performance of MMSE than that of ADAS-Cog in our study and other related studies...
  15. pmc FlyExpress: visual mining of spatiotemporal patterns for genes and publications in Drosophila embryogenesis
    Sudhir Kumar
    Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    Bioinformatics 27:3319-20. 2011
    ..Therefore, FlyExpress is a unique tool for mining spatiotemporal expression patterns in a format readily accessible to the scientific community...
  16. pmc Adaptive diffusion kernel learning from biological networks for protein function prediction
    Liang Sun
    Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA
    BMC Bioinformatics 9:162. 2008
    ..One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data...
  17. doi request reprint Analysis of sampling techniques for imbalanced data: An n=648 ADNI study
    Rashmi Dubey
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA
    Neuroimage 87:220-41. 2014
    ..Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. ..
  18. ncbi request reprint Sparse generalized functional liner model for predicting remission status of depression patients
    Yashu Liu
    Department of Computer Science and Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
    Pac Symp Biocomput 19:364-75. 2014
    ....
  19. pmc Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis
    Yalin Wang
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
    Neuroimage 74:209-30. 2013
    ..This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification...
  20. pmc Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation
    Shuai Huang
    Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287 8809, USA
    Neuroimage 50:935-49. 2010
    ..Our experiments show that the best sensitivity and specificity our method can achieve in AD vs. NC classification are 88% and 88%, respectively...
  21. doi request reprint A subject-independent method for automatically grading electromyographic features during a fatiguing contraction
    Rita Chattopadhyay
    Department of Computer Science and Engineering and with the Center for Cognitive Ubiquitous Computing, Arizona State University, Tempe, AZ 85287, USA
    IEEE Trans Biomed Eng 59:1749-57. 2012
    ..The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases...