Open AccessAn integrated approach to prognosis using protein microarrays and nonparametric methods
Author(s) -
Knickerbocker Tanya,
Chen Jiunn R,
Thadhani Ravi,
MacBeath Gavin
Publication year - 2007
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1038/msb4100167
Subject(s) - multivariate statistics , biology , multivariate analysis , nonparametric statistics , dna microarray , set (abstract data type) , disease , bioinformatics , parametric statistics , computational biology , medicine , oncology , machine learning , statistics , computer science , genetics , gene , mathematics , gene expression , programming language
Over the past several years, multivariate approaches have been developed that address the problem of disease diagnosis. Here, we report an integrated approach to the problem of prognosis that uses protein microarrays to measure a focused set of molecular markers and non‐parametric methods to reveal non‐linear relationships among these markers, clinical variables, and patient outcome. As proof‐of‐concept, we applied our approach to the prediction of early mortality in patients initiating kidney dialysis. We found that molecular markers are not uniformly prognostic, but instead vary in their value depending on a combination of clinical variables. This may explain why reports in this area aiming to identify prognostic markers, without taking into account clinical variables, are either conflicting or show that markers have marginal prognostic value. Just as treatments are now being tailored to specific subsets of patients, our results show that prognosis can also benefit from a ‘personalized’ approach.