MicroRNAs (miRNAs) are a class of small, endogenous RNAs that are 21–25 nucleotides in length. In animals and plants, miRNAs target specific genes for degradation or translation repression. Discovering disease-related miRNA is fundamental for understanding the pathogenesis of diseases. The association between miRNA and a disease is mainly determined via biological investigation, which is complicated by increased biological information due to big data from different databases. Researchers have utilized different computational methods to harmonize experimental approaches to discover miRNA that articulates restrictively in specific environmental situations. In this work, we present a prediction model that is based on the theory of path features and random walk to obtain a relevancy score of miRNA-related disease. In this model, highly ranked scores are potential miRNA-disease associations. Features were extracted from positive and negative samples of miRNA-disease association. Then, we compared our method with other presented models using the five-fold cross-validation method, which obtained an area under the receiver operating characteristic curve of 88.6%. This indicated that our method has a better performance compared to previous methods and will help future biological investigations.

Automatic detection of transparent materials (e.g., glass, plastic, etc.) is essential in many computer vision tasks. For example, a robot could use such a system to navigate around transmissive materials or operate tasks with these materials without causing damage. Nevertheless, it is challenging task as such materials exhibit less texture or background scenes dominate visual perception. Existing methods used either handengineered or leaned features to detect and segment transparent objects. We argue that pixel-wise detection and segmentation of transmissive materials improve detection performance and provide the fine-grained information compared to detecting bounding boxes of objects (i.e., localisation task). In this paper, we leverage a robust and state-of-the-art instance segmentation method namely, Mask R-CNN, in order to detect transparent materials. To be specific, we train the model on a new dataset with an evaluation based on publicly available dataset. Experimental results show that the adopted method significantly enhances the performance of transparent material detection. In particular, the resulting binary masks provides the pixel-level information for an improved understanding and analysis of transparency.

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