亚型
计算机科学
机器学习
人工智能
特征选择
变压器
深度学习
癌症医学
计算生物学
生物信息学
癌症
生物
电压
程序设计语言
遗传学
量子力学
物理
作者
Anwar A. Khan,Boreom Lee
标识
DOI:10.1016/j.eswa.2023.120047
摘要
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.
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