Identifying the association between long noncoding RNA (lncRNA) and micro-RNA (miRNA) is of great significance for the treatment of diseases by interfering with the combination of miRNA and messenger RNA (mRNA). Although many efforts and resources have been invested to identify lncRNA-miRNA associations (LMAs), clinical trials are still expensive and laborious. Nevertheless, the experiments also need to consult a large number of side effects. Therefore, novel computer-aided models are urgently needed to predict LMAs. This paper proposed a semantic embedded bipartite graph network for predicting lncRNA-miRNA associations (SEBGLMA), which provided a novel feature extraction method by integrating K-mer segmentation, word2vec, Gaussian interaction profile (GIP), and graph convolution network (GCN). Concretely, the attribute characteristics of RNA sequences are extracted by K-mer segmentation and word2vec modules. Afterward, the adjacent matrix is completed by GIP self-similarity. Then, the attribute characteristics and adjacent matrix are fed into GCN for embedding behavior features. Finally, the features are sent into the rotation forest (RoF) for detecting potential LMAs. The average accuracy, precision, sensitivity, specificity, Matthews correlation coefficient, and F1-Score are 87.09%, 87.66%, 87.03%, 87.84%, 74.18%, and 86.99% on the benchmark data set. For fairly validating the performance of our model, we conducted various comparisons with different classifiers. Furthermore, the case studies of hsa-miR-497-5P and NONHSAT022145.2 are also established. The results of comparisons and case studies further illustrated that our method is anticipated to become a robust and reliable tool for the identification of LMAs.
As COVID-19 continues to put pressure on the global healthcare industry, using artificial intelligence to analyze chest X-rays (CXR) has become an effective way to diagnose the virus and treat patients. Despite that many studies have made significant progress in COVID-19 detection, accurately segmenting infected regions with variable locations and scales from COVID-19 CXR remains challenging. Therefore, this paper proposes a novel framework for COVID-19 CXR image segmentation. Specifically, we design a loop residual module to cyclically extract feature information in the process of encoding and decoding splicing, avoiding the loss of complex semantic information in network computing. At the same time, an absolute position information coding block is proposed to strengthen the position information of feature pixels. Moreover, a hybrid attention module is designed to establish semantic associations between channels and multi-scale spaces. Better feature representation is formed by the fusion of location and scale information to alleviate the impact of variable infection regions on segmentation performance. Extensive experiments are conducted on the public COVID-19 CXR dataset COVID-Qu-Ex, and the results show that our network is leading and robust compared to other networks in COVID-19 segmentation.
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