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