Feedforward Deep Learning Optimizer-based RNASeq Women's Cancers Detection with a Hybrid Classification Models for Biomarker Discovery

Research Abstract

Women's cancers, signified by breast adenocarcinoma and non-small-cell lung cancers, are a significant threat to women's health. Across the globe, the leading cause of death for women is a group of tumors referred to as "female-oriented cancers". The most recent researches in the classification of molecular tumors is the analysis of women's cancers using RNA-Seq data for precision cancer diagnoses. Furthermore, discovering the different genes’ patterns behaviors will lead to predict the cancer-specific biomarkers to early diagnosis and detection of cancer-specific in women. An overfit model will be resulted due to the high-dimensional data of RNASeq from a small samples of data. In this work, we propose a filter-based selection approach for a deep learning-based classification model. In addition, hybrid classification models have been proposed to be compared with the new modified deep learning model. The Experiments’ analysis showed that the proposed filter-based selection approach for a deep learningbased classification model performed better than the other hybrid models in terms of performance evaluation metrics, with an accuracy of 96.7% for RNA-Seq breast adenocarcinoma data and 95.5% for RNA-Seq non-small-cell lung cancer data.

Research Keywords

Women's cancers; RNA-Seq; deep learning; molecular tumor; hybrid classification models