Paper Title
Breast Cancer Prognostic 2-Class Classification of Multidimensional Molecular Data

Abstract
High-throughput experiments like microarrays and next-generation sequencing have generated large amounts of molecular data. One of the important data mining method in large scale data analysis is classification task. Here we report an integrative analysis of gene expression profiling measured by DNA microarrays with high-throughput sequencing (ChIP-seq) and protein expression profiling by reverse phase protein array for Breast Invasive Carcinoma data. We describe a two class analysis of breast invasive carcinoma to identify molecular markers connected with the patient dead risk. Our results have shown that integrated analysis with proper feature selection and classification techniques used for merged molecular data can improve the classification accuracy. Index Termsó Classification, breast invasive carcinoma, feature selection, DNA microarrays.