Collaborative Research: Sufficient Dimension Reduction for High Dimensional Data with Applications in Bioinformatics

点击翻译按钮获取中文摘要

Abstract proposals: 0405360 and 0405681PIs: Cook & LiCOLLABORATIVE RESEARCH: Dimension Reduction with application to bioinformaticsAs represented in the existing literature, sufficient dimension reduction (SDR) encompasses model-free methods for linearly reducing the dimension of the predictor vector in regression and classification problems without loss of information. SDR methodology has a brief but striking record of success, although its inferential foundations are relatively narrow and the restriction to linear reductions can be limiting in some applications. The investigators and their co-authors expand the inferential foundations of SDR through the development of optimal methods within the context of linear reduction and the study of new nonlinear reduction methods. The new optimal reduction methods permit the investigators to derive model-free tests of conditional independence, which are roughly data-analytic equivalents of t-tests on coefficients in model-based linear regression. They emphasize bioinformatics applications in general and the analysis of data from high-throughput genomic technologies in particular.The computer revolution has produced an unprecedented capacity for data generation, processing and storage, with the consequence that data reduction is paramount in many research areas and business applications. For instance, genomic technology can produce measurements for thousands of genes across multiple tissue samples, and WalMart makes over 20 million transactions daily. The development of diagnostics for breast cancer based on fine needle aspiration can involve the study of numerous measurements on extracted cells across hundreds of patients. In response to this proliferation of information, the investigators and their colleagues study methods for reducing data to an essential core. Their approach is unique because their overarching goal is reduction without loss of information on the issues under consideration. In the development of diagnostics for breast cancer, this goal translates into reducing numerous cell measurements to an index that can be used to classify a breast mass as malignant or benign without loss of information, allowing the physician to present a more informed recommendation to the patient.