A neural network-based biomarker association information extraction approach for cancer classification.

A number of different approaches based on high-throughput data have been developed for cancer classification. However, these methods often ignore the underlying correlation between the expression levels of different biomarkers which are related to cancer. From a biological viewpoint, the modeling of these abnormal associations between biomarkers will play an important role in cancer classification. In this paper, we propose an approach based on the concept of Biomarker Association Networks (BAN) for cancer classification. The BAN is modeled as a neural network, which can capture the associations between the biomarkers by minimizing an energy function. Based on the BAN, a new cancer classification approach is developed. We validate the proposed approach on four publicly available biomarker expression datasets. The derived Biomarker Association Networks are observed to be significantly different for different cancer classes, which help reveal the underlying deviant biomarker association patterns responsible for different cancer types. Extensive comparisons show the superior performance of the BAN-based classification approach over several conventional classification methods.