  Cancer and its subtypes constitute approximately 30% of all causes of death
globally and display a wide range of heterogeneity in terms of clinical and
molecular responses to therapy. Molecular subtyping has enabled the use of
precision medicine to overcome these challenges and provide significant
biological insights to predict prognosis and improve clinical decision-making.
Over the past decade, conventional machine learning (ML) and deep learning (DL)
algorithms have been widely espoused for the classification of cancer subtypes
from gene expression datasets. However, these methods are potentially biased
toward the identification of cancer biomarkers. Hence, an end-to-end deep
learning approach, DeepGene Transformer, is proposed which addresses the
complexity of high-dimensional gene expression with a multi-head self-attention
module by identifying relevant biomarkers across multiple cancer subtypes
without requiring feature selection as a pre-requisite for the current
classification algorithms. Comparative analysis reveals that the proposed
DeepGene Transformer outperformed the commonly used traditional and
state-of-the-art classification algorithms and can be considered an efficient
approach for classifying cancer and its subtypes, indicating that any
improvement in deep learning models in computational biologists can be
reflected well in this domain as well.
