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Variations in DNA copy number carry information on the modalities of genome evolution and mis-regulation of DNA replication in cancer cells. Their study can help localize tumor suppressor genes, distinguish different populations of cancerous cells, and identify genomic variations responsible for disease phenotypes. A number of different high throughput(More)
Recent advances in genomics have underscored the surprising ubiquity of DNA copy number variation (CNV). Fortunately, modern genotyping platforms also detect CNVs with fairly high reliability. Hidden Markov models and algorithms have played a dominant role in the interpretation of CNV data. Here we explore CNV reconstruction via estimation with a(More)
Databases Abstract In this paper, we propose to use statistical methods to estimate unknown attribute values in databases, as compared to assigning possible values at users' discretion in common practice. Regression models and classification analysis are introduced for estimating continuous and categorical unknown attribute values, respectively. Procedures(More)
Personalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics. We developed a personalized cancer therapy(More)
The prostate specific membrane antigen (PSMA) is broadly overexpressed on prostate cancer (PCa) cell surfaces. In this study, we report the synthesis, characterization, in vitro binding assay, and in vivo magnetic resonance imaging (MRI) evaluation of PSMA targeting superparamagnetic iron oxide nanoparticles (SPIONs). PSMA-targeting polypeptide CQKHHNYLC(More)
We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum-likelihood estimation. In contrast with univariate regression and interval-mapping methods, our model requires(More)
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