Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

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Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values, with many computer-aided diagnosis systems using common deep learning methods that have been proposed to save time and labour. Even though deep learning methods are an end-to-end method, they perform exceptionally well given a large dataset and often show relatively inferior results for a small dataset. In contrast, traditional feature extraction methods have greater robustness and perform well with a small/medium dataset. Moreover, a texture representation-based global approach is commonly used to classify histological tissue images expect in explicit segmentation to extract the structure properties. Considering the scarcity of medical datasets and the usefulness of texture representation, we would like to integrate both the advantages of deep learning and traditional machine learning, i.e., texture representation. To accomplish this task, we proposed a classification model to detect renal cancer using a histopathology dataset by fusing the features from a deep learning model with the extracted texture feature descriptors. Here, five texture feature descriptors from three texture feature families were applied to complement Alex-Net for the extensive validation of the fusion between the deep features and texture features. The texture features are from (1) statistic feature family: histogram of gradient, gray-level cooccurrence matrix, and local binary pattern; (2) transform-based texture feature family: Gabor filters; and (3) model-based texture feature family: Markov random field. The final experimental results for classification outperformed both Alex-Net and a singular texture descriptor, showing the effectiveness of combining the deep features and texture features in renal cancer detection.