REDalign – accurate RNA structural alignment using residual encoder-decoder network
RNA molecules are essential for various biological processes, and studying them helps scientists understand how…
Browsing: Analysis Pipelines
scHetG – Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network
Single-cell RNA sequencing (scRNA-seq) is a powerful technique that allows scientists to study individual cells…
ZINBStein – shrinkage estimation of gene interaction networks in single-cell RNA sequencing data
Understanding how genes interact with one another is crucial for unraveling the complexities of biological…
GRUpred-m5U – a robust deep learning approach for identification of RNA 5-methyluridine sites
RNA molecules play crucial roles in various processes, including gene expression, protein synthesis, and cellular…
Memento – method of moments framework for differential expression analysis of single-cell RNA sequencing data
Single-cell RNA sequencing (scRNA-seq) is a powerful technique that allows scientists to study how genes…
scCaT – an explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing
Sepsis is a serious medical condition that occurs when the body has an extreme response…
SELF-Former – multi-scale gene filtration transformer for single-cell spatial reconstruction
Scientists have developed incredible tools to study how genes are expressed in individual cells, giving…
scCTS – identifying the cell type-specific marker genes from population-level single-cell RNA-seq
Single-cell RNA sequencing (scRNA-seq) is an exciting tool in biology that lets researchers dive deep…
McGill researchers introduce a scalable, cost-effective solution for single-cell profiling
An innovative computational tool, scSemiProfiler, makes powerful single-cell sequencing technology more accessible…
spacedeconv – deconvolution of tissue architecture from spatial transcriptomics
Understanding how tissues function is key to learning about both health and disease. Tissues are…