A5078283432- Cancer-related molecular mechanisms research
- RNA Research and Splicing
- Advanced Neural Network Applications
- RNA and protein synthesis mechanisms
- Remote-Sensing Image Classification
- Domain Adaptation and Few-Shot Learning
- RNA modifications and cancer
- Advanced Image and Video Retrieval Techniques
- Genomics and Phylogenetic Studies
- MicroRNA in disease regulation
- Machine Learning in Bioinformatics
- Circular RNAs in diseases
University of Electronic Science and Technology of China
2022-2024
Abstract RNA‐dependent liquid‐liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis frontotemporal dementia, making their identification crucial. However, conventional biochemistry‐based methods for...
With the development of deep learning, performance image semantic segmentation in remote sensing has been constantly improved. However, usually degrades while testing on different datasets because domain gap. To achieve feasible performance, extensive pixel-wise annotations are acquired a new environment, which is time-consuming and labor-intensive. Therefore, unsupervised adaptation (UDA) proposed to alleviate effort labeling. most previous approaches based outdated network architectures...
Remote sensing object detection is a basic yet challenging task in remote image understanding. In contrast to horizontal objects, objects are commonly densely packed with arbitrary orientations and highly complex backgrounds. Existing methods lack an effective mechanism exploit these characteristics distinguish various targets. Unlike mainstream approaches ignoring spatial interaction among targets, this paper proposes shape-adaptive repulsion constraint on point representation capture...
Accurate prediction of subcellular localization viral proteins is crucial for understanding their functions and developing effective antiviral drugs. However, this task poses a significant challenge, especially when relying on expensive time-consuming classical biological experiments. In study, we introduced computational model called E-MuLA, based deep learning network that combines multiple local attention modules to enhance feature extraction from protein sequences. The superior...
Over the years, extensive research has highlighted functional roles of small nucleolar RNAs in various biological processes associated with development complex human diseases. Therefore, understanding existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis treatment. However, classical experiments identifying snoRNA-disease associations are expensive time-consuming. there an urgent need cost-effective computational techniques that can enhance...
Long non-coding RNAs (lncRNAs) have emerged as significant contributors to the regulation of various biological processes, and their dysregulation has been linked a variety human disorders. Accurate prediction potential correlations between lncRNAs diseases is crucial for advancing disease diagnostics treatment procedures. The authors introduced novel computational method, iGATTLDA, lncRNA-disease associations. model utilised lncRNA similarity matrices, with known associations represented in...