Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data ha...
The rapid development of RNA vaccines and therapeutics puts forward intensive requirements on the sequence design of RNAs. RNA sequence design, or RNA inverse folding, aims to generate RNA sequences that can fold into specific target structures. To d...
High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules,...
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment ...
Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detectin...
In crowding, perception of a target deteriorates in the presence of nearby elements. As the entire stimulus configuration across large parts of the visual field influences crowding and not just nearby elements, low-level explanations, such as local p...
Date fruits are a primary agricultural product that comes in a variety of textures, colors, and tastes; hence, the correct classification is crucial for quality control, automatic sorting, and commercial applications. Deep learning has surely shown c...
The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter-laboratory di...
BACKGROUNDS: Exploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of ...
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