Research Topics
| Mary Qu YangSummaryAffiliation: National Institutes of Health Country: USA Publications
| Collaborators
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Detail Information
Publications
Promoting synergistic research and education in genomics and bioinformaticsJack Y Yang
Harvard University, PO Box 400888, Cambridge, Massachusetts 02140 0888, USA
BMC Genomics 9:I1. 2008....- Genomics, molecular imaging, bioinformatics, and bio-nano-info integration are synergistic components of translational medicine and personalized healthcare researchJack Y Yang
Harvard Medical School, Harvard University, Cambridge, Massachusetts 02115, USA
BMC Genomics 9:I1. 2008..b>Mary Qu Yang (IEEE BIBM workshop keynote lecturer on new initiatives of detecting microscopic disease using machine learning .. - 3D protein structure prediction with genetic tabu search algorithmXiaolong Zhang
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, PR China
BMC Syst Biol 4:S6. 2010..Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task... - BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence featuresLiangjiang Wang
Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
BMC Syst Biol 4:S3. 2010..However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences... - Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumesJack 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... - Selecting subsets of newly extracted features from PCA and PLS in microarray data analysisGuo Zheng Li
Department of Control Science and Engineering, Tongji University, Shanghai 201804, PR China
BMC Genomics 9:S24. 2008..While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods... - Word-based characterization of promoters involved in human DNA repair pathwaysJens Lichtenberg
Bioinformatics Laboratory, School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, USA
BMC Genomics 10:S18. 2009.... - Investigation of transmembrane proteins using a computational approachJack Y Yang
Department of Radiology, Brigham and Women s Hospital and Harvard Medical School, Boston, MA 02115, USA
BMC Genomics 9:S7. 2008.... - Comparison of feature selection and classification for MALDI-MS dataQingzhong Liu
Department of Computer Science, New Mexico Tech, Socorro, NM 87801 USA
BMC Genomics 10:S3. 2009..The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data... - A comparative study of different machine learning methods on microarray gene expression dataMehdi 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... - Cross-species mapping of bidirectional promoters enables prediction of unannotated 5' UTRs and identification of species-specific transcriptsHelen Piontkivska
2Department of BiologicalSciences, Kent State University, Kent, Ohio 44242, USA
BMC Genomics 10:189. 2009.... - Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair compositionTanwir 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... - Supervised learning-based tagSNP selection for genome-wide disease classificationsQingzhong 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... - Prediction of DNA-binding residues from protein sequence information using random forestsLiangjiang Wang
Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
BMC Genomics 10:S1. 2009..With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data... - Improving prediction accuracy of tumor classification by reusing genes discarded during gene selectionJack Y Yang
Harvard Medical School, Harvard University, Cambridge, Massachusetts 02140 0888 USA
BMC Genomics 9:S3. 2008.... - Predicting protein disorder by analyzing amino acid sequenceJack 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... - A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesionsJack Y Yang
Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Boston, MA 02115, USA
BMC Genomics 9:S23. 2008.... - Dimension reduction with redundant gene elimination for tumor classificationXue Qiang Zeng
School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China
BMC Bioinformatics 9:S8. 2008..Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of amounts of redundant features in the microarray data set... - Asymmetric bagging and feature selection for activities prediction of drug moleculesGuo 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... 
Lecture notes: 2010 and beyond, the decade of high-performance computing for the next-generation sequence analysisMary Qu Yang
Department of Health and Human Services, Oak Ridge, DOE, United States National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20852, USA
Int J Comput Biol Drug Des 2:204-6. 2009..We focus mainly on the development of high-performance genetic algorithms based on multi-core technology as an example and open to fast-moving competing platforms to be emerged...- Promoting inter/multidisciplinary education and research in bioinformatics, systems biology and intelligent computingMary Qu Yang
Department of Health and Human Services, United States National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20852, USA
Int J Comput Biol Drug Des 2:207-20. 2009..The synergic research on integrating bioinformatics and systems biology facilitates the advances in biology and medicine... - High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformaticsMary Qu Yang
National Human Genome Research Institute, National Institutes of Health NIH, U S Department of Health and Human Services, Bethesda, MD 20892, USA
BMC Genomics 10:I1. 2009..The theme of the conference to promote synergistic research and education has been achieved successfully... - Diversity of core promoter elements comprising human bidirectional promotersMary Qu Yang
National Human Genome Research Institute, National Institutes Health, Rockville, MD 20852, USA
BMC Genomics 9:S3. 2008..To define the core promoter elements of bidirectional promoters in human, we mapped motifs for TATA, INR, BRE, DPE, INR, as well as CpG-islands... - Prediction-based approaches to characterize bidirectional promoters in the mammalian genomeMary Qu Yang
National Human Genome Research Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD 20892, USA
BMC Genomics 9:S2. 2008..We proposed that knowledge gleaned from those methods could be further refined using a multiple class predictor to separate classes of promoter elements from enhancers or nonfunctional DNA... - Comparative analyses of bidirectional promoters in vertebratesMary Qu Yang
Genome Technology Branch, NHGRI, NIH, MD, USA
BMC Bioinformatics 9:S9. 2008..In this report we utilize orthology assignments for pairs of genes co-regulated by bidirectional promoters to map the ancestral history of the promoter regions... - ILOOP--a web application for two-channel microarray interwoven loop designMehdi 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... 
Identification of Intrinsically Unstructured Proteins using hierarchical classifierJack 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...