scImmuneCo – a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data
The immune system is made up of many different types of cells, each with its own specialized role. During infection, inflammation, or autoimmune disease, these cells do not all respond in the same way. Understanding those differences is essential ...
AI model improves prediction of immunotherapy response across cancers
Immune checkpoint inhibitors have changed the treatment of many cancers by helping the immune system recognize and attack tumor cells. While these therapies can be remarkably effective, only a portion of patients benefit from them. Finding reliable ways to ...
Improving gene expression analysis with APA-seq data
Gene expression studies often focus on measuring how much of a gene is being produced in different cells or tissues. However, genes can also be regulated after transcription through processes that affect how RNA molecules are processed. One important ...
MEBOCOST – mapping metabolite-mediated intercellular communications using single-cell RNA-seq
Cells are constantly communicating with each other to keep tissues healthy and functioning properly. Much of what we know about cell to cell communication focuses on proteins, such as hormones or signaling molecules that bind to receptors on neighboring ...
Integrating single-cell multiomics to link genetic variants with cell-type regulatory networks
Understanding how genetic differences influence disease and aging at the cellular level is one of the biggest challenges in modern biology. While large genetic studies called genome-wide association studies, or GWASs, can point to DNA variants linked to conditions ...
GraphComm – predicting cell cell communication using a graph based deep learning method in single cell RNA sequencing data
A team led by researchers at the Princess Margaret Cancer Centre in Toronto have introduced a powerful new approach to studying how cells communicate with one another. Their deep learning framework, called GraphComm, uses single-cell RNA sequencing (RNA-seq) data ...
HALO – hierarchical causal modeling for single cell multi-omics data
Researchers at the University of Pittsburgh have developed an innovative framework called HALO that reveals how changes in gene activity and chromatin structure interact over time. While open chromatin often signals active transcription, these two processes don’t always move ...
Cisformer: a scalable cross-modality generation framework for decoding transcriptional regulation at single-cell resolution
Understanding how genes are turned on or off inside individual cells is a key challenge in modern biology. Researchers from Tongji University have developed a powerful new computational model called Cisformer to help solve this problem. In single-cell biology, ...
KEGNI – knowledge graph enhanced framework for gene regulatory network inference
Mapping out how genes are controlled inside cells is one of the most important tasks in modern biology. Genes rarely work in isolation. Instead, they operate within large regulatory networks, where certain genes act like “switches” that turn others ...
MulNet – mapping gene networks to understand cancer
Understanding how genes interact is crucial for studying diseases like cancer, but most existing models only capture a small portion of these complex relationships. Researchers at the Beijing Institute of Lifeomics have developed MulNet, a new computational framework that ...
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Most Shared Posts
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- Upcoming Workshop – Advanced Topics in Single Cell RNA-Seq Analysis: Multiomics ATAC-Seq & RNA-Seq
- BAP-seq – bioorthogonal masked acylating agents for proximity-dependent RNA labelling
- Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment
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