Work

Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

Year: 2012

Type: conference-paper

Abstract: . We present a machine learning system for the differential diagnosis of benign adrenocortical adenoma (ACA) vs. malignant adrenocortical carcinoma (ACC). The data employed for the classification are urinary excretion values of 32 steroid metabolites. We apply prototypebased classification techniques to discriminate the classes, in particular, we use modifications of Generalized Learning Vector Quantization including matrix relevance learning. The obtained system achieves high sensitivity and specificity and outperforms previously used approaches for the detection of adrenal malignancy. Moreover, the method identifies a subset of most discriminative markers which facilitates its future use as a noninvasive high-throughput diagnosis tool. 1

Source: University of Groningen research database (University of Groningen / Centre for Information Technology)