Jack Y Yang

Summary

Affiliation: Harvard University
Country: USA

Publications

  1. ncbi Promoting synergistic research and education in genomics and bioinformatics
    Jack Y Yang
    Harvard University, PO Box 400888, Cambridge, Massachusetts 02140 0888, USA
    BMC Genomics 9:I1. 2008
  2. ncbi Genomics, molecular imaging, bioinformatics, and bio-nano-info integration are synergistic components of translational medicine and personalized healthcare research
    Jack Y Yang
    Harvard Medical School, Harvard University, Cambridge, Massachusetts 02115, USA
    BMC Genomics 9:I1. 2008
  3. ncbi Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumes
    Jack Y Yang
    Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
    BMC Genomics 9:S9. 2008
  4. ncbi Investigation of transmembrane proteins using a computational approach
    Jack Y Yang
    Department of Radiology, Brigham and Women s Hospital and Harvard Medical School, Boston, MA 02115, USA
    BMC Genomics 9:S7. 2008
  5. ncbi A comparative study of different machine learning methods on microarray gene expression data
    Mehdi Pirooznia
    Department of Biological Sciences, University of Southern Mississippi, Hattiesburg 39406, USA
    BMC Genomics 9:S13. 2008
  6. ncbi Protein disorder prediction at multiple levels of sensitivity and specificity
    Joshua Hecker
    School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
    BMC Genomics 9:S9. 2008
  7. ncbi Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition
    Tanwir Habib
    Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
    BMC Genomics 9:S16. 2008
  8. ncbi The unfoldomics decade: an update on intrinsically disordered proteins
    A Keith Dunker
    Center for Computational Biology and Bioinformatics, Indiana University Schools of Medicine and Informatics, Indianapolis, IN 46202, USA
    BMC Genomics 9:S1. 2008
  9. ncbi Supervised learning-based tagSNP selection for genome-wide disease classifications
    Qingzhong Liu
    Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
    BMC Genomics 9:S6. 2008
  10. ncbi Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection
    Jack Y Yang
    Harvard Medical School, Harvard University, Cambridge, Massachusetts 02140 0888 USA
    BMC Genomics 9:S3. 2008

Collaborators

Detail Information

Publications18

  1. ncbi Promoting synergistic research and education in genomics and bioinformatics
    Jack Y Yang
    Harvard University, PO Box 400888, Cambridge, Massachusetts 02140 0888, USA
    BMC Genomics 9:I1. 2008
    ....
  2. ncbi Genomics, molecular imaging, bioinformatics, and bio-nano-info integration are synergistic components of translational medicine and personalized healthcare research
    Jack Y Yang
    Harvard Medical School, Harvard University, Cambridge, Massachusetts 02115, USA
    BMC Genomics 9:I1. 2008
    ....
  3. ncbi Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumes
    Jack Y Yang
    Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
    BMC Genomics 9:S9. 2008
    ..For many years, it has been well concluded that radio-sensitivities of tumors upon radiotherapy decrease according to the sizes of tumors and RT models based on Poisson statistics have been used extensively to validate clinical data...
  4. ncbi Investigation of transmembrane proteins using a computational approach
    Jack Y Yang
    Department of Radiology, Brigham and Women s Hospital and Harvard Medical School, Boston, MA 02115, USA
    BMC Genomics 9:S7. 2008
    ....
  5. ncbi A comparative study of different machine learning methods on microarray gene expression data
    Mehdi Pirooznia
    Department of Biological Sciences, University of Southern Mississippi, Hattiesburg 39406, USA
    BMC Genomics 9:S13. 2008
    ..However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results...
  6. ncbi Protein disorder prediction at multiple levels of sensitivity and specificity
    Joshua Hecker
    School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
    BMC Genomics 9:S9. 2008
    ..However, most of the software packages use a pre-defined threshold to select ordered or disordered residues. In many situations, users need to choose ordered or disordered residues at different sensitivity and specificity levels...
  7. ncbi Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition
    Tanwir Habib
    Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
    BMC Genomics 9:S16. 2008
    ..Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy...
  8. ncbi The unfoldomics decade: an update on intrinsically disordered proteins
    A Keith Dunker
    Center for Computational Biology and Bioinformatics, Indiana University Schools of Medicine and Informatics, Indianapolis, IN 46202, USA
    BMC Genomics 9:S1. 2008
    ..The results from genome-wide predictions of intrinsic disorder and the results from other bioinformatics studies of intrinsic disorder are demanding attention for these proteins...
  9. ncbi Supervised learning-based tagSNP selection for genome-wide disease classifications
    Qingzhong Liu
    Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
    BMC Genomics 9:S6. 2008
    ..To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers...
  10. ncbi Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection
    Jack Y Yang
    Harvard Medical School, Harvard University, Cambridge, Massachusetts 02140 0888 USA
    BMC Genomics 9:S3. 2008
    ....
  11. ncbi Predicting protein disorder by analyzing amino acid sequence
    Jack Y Yang
    Harvard Medical School, Harvard University, Cambridge, MA 02115, USA
    BMC Genomics 9:S8. 2008
    ..These proteins and regions are known as Intrinsically Unstructured Proteins (IUP). IUP have been associated with a wide range of protein functions, along with roles in diseases characterized by protein misfolding and aggregation...
  12. ncbi A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesions
    Jack Y Yang
    Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Boston, MA 02115, USA
    BMC Genomics 9:S23. 2008
    ....
  13. ncbi Transcriptome profiling of Saccharomyces cerevisiae mutants lacking C2H2 zinc finger proteins
    Jinghe Mao
    Department of Biology, Tougaloo College, Tougaloo, MS 39174, USA
    BMC Genomics 9:S14. 2008
    ..In this study we completed a transcriptomic profiling of three mutants lacking C2H2 zinc finger proteins, ypr013cDelta,ypr015cDelta and ypr013cDeltaypr015cDelta...
  14. ncbi In silico comparison of transcript abundances during Arabidopsis thaliana and Glycine max resistance to Fusarium virguliforme
    Jiazheng Yuan
    Department of Plant, Soil Sciences and Agriculture System, Southern Illinois University at Carbondale, Carbondale, IL 62901, USA
    BMC Genomics 9:S6. 2008
    ..Deciphering the variations among transcript abundances (TAs) of functional orthologous genes of soybean and A. thaliana involved in the interaction will provide insights into plant resistance to F. viguliforme...
  15. ncbi ILOOP--a web application for two-channel microarray interwoven loop design
    Mehdi Pirooznia
    Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
    BMC Genomics 9:S11. 2008
    ..It is freely available from http://mcbc.usm.edu/iloop...
  16. ncbi Asymmetric bagging and feature selection for activities prediction of drug molecules
    Guo Zheng Li
    Institute of Systems Biology, Shanghai University, Shanghai 200444, China
    BMC Bioinformatics 9:S7. 2008
    ..With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation...
  17. ncbi Identification of Intrinsically Unstructured Proteins using hierarchical classifier
    Jack Y Yang
    Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts 02114, USA
    Int J Data Min Bioinform 2:121-33. 2008
    ..The classifier has been benchmarked against industrial standard PONDR VLXT on out-of-sample data by external evaluators. The IUP predictor is a viable alternative software tool for identifying intrinsic unstructured regions and proteins...
  18. ncbi 2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research
    Jack Y Yang
    Department of Radiation Oncology, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, Massachusetts 02114, USA
    BMC Genomics 11:I1. 2010
    ..Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine...