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single-cell genomics
Trends
- 1Graph Transformer Model Aims to Sharpen RNA Velocity Predictions▼Graph Transformer Model Aims to Sharpen RNA Velocity Predictions in Single-Cell Genomics
Researchers have introduced a graph transformer model designed to improve RNA velocity predictions in single-cell genomics. RNA velocity estimates the future state of individual cells, but existing methods struggle with noisy data and complex cell trajectories. By applying transformer-based deep learning to gene regulatory graphs, the new approach aims to produce more accurate predictions of how cells develop and differentiate. If validated, it could strengthen research in developmental biology and disease studies, areas where precise modeling of cell dynamics matters.
- 2New Study Maps Human Meningeal Development in Detail▼Spatially resolved multiomics of human meningeal development reveal lineage and disease dynamics
Researchers publishing in Nature have used spatially resolved multiomics to map how the human meninges, the protective membranes surrounding the brain and spinal cord, develop. The work traces cell lineages during development and links them to disease dynamics, offering a reference for understanding neurological disorders tied to meningeal biology. It is being discussed among researchers following developmental biology and single-cell genomics.
- 310x Genomics Launches Sentira Computational Biology Platform▼10x Genomics Announces Sentira, a New Computational Platform to Turn Complex Biological Data Into Experimental Conclusions
10x Genomics has announced Sentira, a new computational platform designed to convert complex single-cell and spatial biological data into experimental conclusions. The company says the tool aims to simplify analysis for researchers, reducing the gap between raw genomic data and actionable scientific findings. Details on pricing, availability and performance have not yet been widely reported.