The construction and assessment of a statistical model for the prediction of protein assay data.
The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.
Pittman
J
J
Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27708, USA. jennifer@stat.Duke.edu
Sacks
J
J
Young
S Stanley
SS
D019295:Computational Biology; D030562:Databases, Protein; D008956:Models, Chemical; D011506:Proteins / Q000737:chemistry*
D011506:Proteins