Design and implementation of bioinformatics KDD workflow for breast cancer risk evaluation

Abstract

The paper introduces the latest trends in IT and the novel achievements of data science in breast cancer risk evaluation in support of personalized medicine. The idea of this publication is to present the summary and classification of breast cancer risk factors and available breast cancer risk evaluation models and tools. The goal is is to propose a bioinformatics knowledge data discovery (KDD) workflow for breast cancer risk evaluation defining the type of risks in three categories: high, increased and low risk. The breast cancer risk evaluation workflow will provide intelligent decision making in support of personalized therapy associated to the type of risk. The proposed workflow is planned to a be part of the operational phase of intelligent method for adaptive in silico knowledge data discovery where the computation is organized on the basis of integrated in silico KDD workflows as a part of the research project DN07/24, financially supported by the Bulgarian National Science Fund.