This article provides a comprehensive review of deep learning applications in bioinformatics, categorizing research by bioinformatics domains (omics, biomedical imaging, biomedical signal processing) and deep learning architectures (deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures). The authors highlight the challenges and advancements in each domain, emphasizing the potential of deep learning to extract valuable insights from large biomedical datasets. They discuss the theoretical and practical issues, such as limited and imbalanced data, the need for interpretability, and the selection of appropriate deep learning architectures. The article also explores multimodal deep learning and strategies for accelerating deep learning models. Finally, it suggests future research directions, including the integration of traditional deep learning architectures and the development of specialized hardware. This review aims to serve as a valuable resource for researchers interested in applying deep learning to bioinformatics studies.This article provides a comprehensive review of deep learning applications in bioinformatics, categorizing research by bioinformatics domains (omics, biomedical imaging, biomedical signal processing) and deep learning architectures (deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures). The authors highlight the challenges and advancements in each domain, emphasizing the potential of deep learning to extract valuable insights from large biomedical datasets. They discuss the theoretical and practical issues, such as limited and imbalanced data, the need for interpretability, and the selection of appropriate deep learning architectures. The article also explores multimodal deep learning and strategies for accelerating deep learning models. Finally, it suggests future research directions, including the integration of traditional deep learning architectures and the development of specialized hardware. This review aims to serve as a valuable resource for researchers interested in applying deep learning to bioinformatics studies.