The construction and assessment of a statistical model for the prediction of protein assay data

Jennifer L Clarke, J. Sacks, S. Stanley Young

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)729-741
Number of pages13
JournalJournal of Chemical Information and Computer Sciences
Volume42
Issue number3
DOIs
StatePublished - May 1 2002
Externally publishedYes

Fingerprint

Bioinformatics
Assays
Proteins
Singular value decomposition
Splines
Experiments
experiment
methodology
ability
Statistical Models
Values

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

The construction and assessment of a statistical model for the prediction of protein assay data. / Clarke, Jennifer L; Sacks, J.; Young, S. Stanley.

In: Journal of Chemical Information and Computer Sciences, Vol. 42, No. 3, 01.05.2002, p. 729-741.

Research output: Contribution to journalArticle

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