Long non-coding RNAs (lncRNAs) are a kind of RNAs with regulation that participate fundamental cellular processes via diverse mechanisms. Despite the potential importance of lncRNAs in multiple kinds of cancer has been well studied, no comprehensive survey of cancer subtype associated lncRNAs. Here, we performed an array-based transcriptional survey of lncRNAs across 150 lung cancer samples comprising both adenocarcinoma and squamous cell carcinoma, and 306 breast cancer patients with clear clinical information. In lung cancer, 72 lncRNAs are identified to be associated with tumor subtypes and their functions as well as the associated proteins are predicted by constructing coding-non-coding co-expression network. The results suggest that they are mostly related with epidermis development, cell adhesion and response to stimulus. The validation results show the high concordance and confirmed the robust of the identification results. In breast cancer, we found 3 lncRNA genes are associated with estrogen receptor α (ER) positive and ER negative subtypes and tumor histologic grade. Survival (Kaplan-Meier) analysis results suggest that the expression pattern of the 3 lncRNAs is significantly correlated with clinical outcomes. The current study provides the first large-scale survey of lncRNAs within cancer subtypes, and may offer new targets for their diagnosis, therapy and prognosis.
In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deeplearning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, landuse/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.
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