Synergy Network Inference Model Based on Heterogeneous Data Integration

Abstract

Deoxyribonucleic acid DNA microarray is one of the most fascinating technologies in molecular biology, which has been used to measure thousands of genes simultaneously. To date, many researchers have agreed that the dawn of “genomic age” has begun and numerous works have been conducted to enlighten the cellular mechanism in the term of Gene Regulatory Network (GRN). However, there are still deficiencies in fully utilizing microarray data for diagnosis, prognosis and treatment of disease. Microarray data only presents partly independent and insufficient complementary information regarding the view of the whole biological system. Therefore, integrating data from different sources and data type plays an important role in current studies to gain a broad interdisciplinary view of cancer progression. As a result, this study aims to combine different types of data, namely clinical and GRN to infer the progression of breast cancer by developing a synergy network based inference model. The results have shown that this model can further improve the ability of classifier to correctly group patients into its corresponding classes, compare to the used of single data type.