准确的诊断是提供及时和明确的治疗和疾病管理的关键。公认的分子诊断传染病病原体的生物学方法是聚合酶链反应（PCR）。近来，深度学习方法在准确识别与疾病相关的基因以进行诊断、预后和治疗方面发挥了重要作用。这些模型减少了湿实验的时间和成本。因此，已经开发出了复杂计算方法来促进全球主要死因之一的癌症及其他复杂疾病的检测。在本综述中，我们系统评估了基于深度学习技术的多组学数据分析的最新趋势，并讨论了该领域的当前挑战以及探讨先进的深度学习方法及其在应用中的优化对克服这些挑战的重要性。最终，这篇综述推动了用于数据整合的创新深度学习方法的发展，这对于疾病的检测和治疗至关重要。版权所有 © 2023 Wekesa 和 Kimwele。

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.Copyright © 2023 Wekesa and Kimwele.