A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors.

ABSTRACT:

Background

Although microscopic diagnosis has been playing the decisive role in cancer diagnostics, there have been cases in which it does not satisfy the clinical need. Differential diagnosis of malignant and benign thyroid tissues is one such case, and supplementary diagnosis such as that by gene expression profile is expected.

Results

With four thyroid tissue types, i.e., papillary carcinoma, follicular carcinoma, follicular adenoma, and normal thyroid, we performed gene expression profiling with adaptor-tagged competitive PCR, a high-throughput RT-PCR technique. For differential diagnosis, we applied a novel multi-class predictor, introducing probabilistic outputs. Multi-class predictors were constructed using various combinations of binary classifiers. The learning set

SUBMITTER: Yukinawa N 

PROVIDER: S-EPMC1550728 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature