Interactions among circular RNAs (circRNAs) and RNA-binding proteins (RBPs) involve almost all stages of the circRNA life cycle. Therefore, circRNA-RBP binding site identification is extremely important for the regulation of human diseases. Various approaches have been used to identify RBP binding sites on circRNAs. Sadly, these approaches are frequently constrained by insufficient feature learning and poor scalability. As a result, we provide a novel model named iDeep-cancer that predicts circRNA-RBP interactions solely using circRNA sequences. A hybrid deep learning model and feature encoding are used in the iDeep-cancer technique. In order to create the feature space, feature encoding uses four feature extraction techniques while accounting for the chemical makeup of circRNA sequences. The hybrid network includes an improved dense convolutional network (DenseNet), a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (Self-attention). DenseNet is used to learn high-level localized features, and the combination of BiGRU and self-attention captures long-term dependencies in sequences. We conducted ablation tests and compared iDeep-cancer with other cutting-edge techniques on 13 datasets in order to verify its efficacy. Findings indicate that iDeep-cancer performs better than current techniques.

Abstract Clinical diagnosis has high requirements for the resolution of medical images, but most existing medical images super‐ resolution (SR) methods are performed under a known or specific degradation kernel. However, the difference between the actual degradations and their assumed degradation kernels results in a severe performance drop for the advanced SR methods in real applications. This paper proposes a medical image blind super‐resolution model (Med‐BSR) based on an improved degradation process to handle this issue. The model makes each of the degradation factors in medical image blind SR, such as blur, noise, and downsampling, more complex and practical. Specifically, the authors use the random select/combine strategy to randomly arrange and combine the type and order of each degradation factor, which significantly expands the degradation space. The authors also improved the loss function of the primary enhanced super‐resolution generative adversarial networks (ESRGAN) network. The extensive experimental results demonstrate that the authors’ designed model can accurately restore the natural degradation process, which can reconstruct high‐quality SR medical images. It also has a good generalization ability to realistic images simultaneously.

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. The segmentation of Multiple sclerosis (MS) lesions from MR images can establish the basis for subsequent lesion reconstruction, volume estimation, and course evaluation. This study proposes a method for automatically segmenting MS lesions based on 3D convolutional neural network (CNN). The method is divided into two stages, each of which includes two convolution layers and two pooling layers. The alternative lesion voxels are selected in the first stage, while in the second stage, the final lesion voxels are segmented from the lesion voxels which are obtained in the first stage by restricting the conditions. The method has been tested on the MICCAI 2008 and 2016 datasets and compared to the other baseline methods. The experiment results show that the method has better performance than the other baseline methods on different evaluation indicators, including dice similarity coefficient, absolute difference in lesion volume, true positive rate, false positive rate, and predictive positivity value.