Deep learning has become a crucial tool in bioinformatics for analyzing large biomedical datasets. This review summarizes current research applications of deep learning in bioinformatics, categorizing them by domain (omics, biomedical imaging, biomedical signal processing) and deep learning architecture (deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures). The paper discusses theoretical and practical challenges in applying deep learning to bioinformatics, including imbalanced data, interpretation, hyperparameter optimization, and training acceleration. It also highlights the potential of deep learning in various bioinformatics applications, such as protein structure prediction, gene expression regulation, and biomedical imaging. The review emphasizes the importance of deep learning in extracting meaningful insights from complex biomedical data and suggests future research directions. The paper also discusses the use of deep learning libraries, such as TensorFlow and Theano, and the importance of proper data preprocessing and feature extraction in deep learning applications. The review concludes that deep learning has the potential to revolutionize bioinformatics by enabling more accurate and efficient analysis of complex biomedical data.Deep learning has become a crucial tool in bioinformatics for analyzing large biomedical datasets. This review summarizes current research applications of deep learning in bioinformatics, categorizing them by domain (omics, biomedical imaging, biomedical signal processing) and deep learning architecture (deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures). The paper discusses theoretical and practical challenges in applying deep learning to bioinformatics, including imbalanced data, interpretation, hyperparameter optimization, and training acceleration. It also highlights the potential of deep learning in various bioinformatics applications, such as protein structure prediction, gene expression regulation, and biomedical imaging. The review emphasizes the importance of deep learning in extracting meaningful insights from complex biomedical data and suggests future research directions. The paper also discusses the use of deep learning libraries, such as TensorFlow and Theano, and the importance of proper data preprocessing and feature extraction in deep learning applications. The review concludes that deep learning has the potential to revolutionize bioinformatics by enabling more accurate and efficient analysis of complex biomedical data.