Detection of Lung Adenocarcinoma Cancer based on RNA-seq gene expression data using LIMMA and TabNet

Abstract

Lung cancer is one of the deadliest diseases of the world to this date with the highest mortality rate amidst all other forms of cancer. Detection of cancer in early stages is crucial for cancer treatment. Progress in cancer detection has been increasingly made based on gene expression levels, giving insight into making correct and successful treatment decisions, thanks to recent advances in high-throughput sequencing technology such as RNA-seq and the use of several machine learning approaches. However, most of the work on cancer detection uses micro-array data and machine learning models. This paper presents a new methodology based on RNA-seq data which is better at detecting transcripts than micro-array along with Deep Neural Network (Tabnet) to classify human lung cancer.