Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery

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 RNA-Seq 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 learning-based 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.

@article{Ead2022,
  title     = {Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Waleed Mahmoud Ead and Marwa Abouelkhir Abdelazim and Mona Mohamed Nasr},
  doi       = {10.14569/IJACSA.2022.0131233},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131233}
}