SC2Spa – a deep learning based approach to map transcriptome to spatial origins at cellular resolution
Understanding how cells are organized within tissues is crucial to learning how organs function and…
Browsing: deep learning
OmiCLIP – a visual–omics foundation model to bridge histopathology with spatial transcriptomics
Researchers are constantly looking for better ways to understand how genes function in the context…
DeepSAP – improved RNA-seq alignment by integrating transcriptome guidance with transformer-based splice junction scoring
RNA sequencing (RNA-seq) has opened up new frontiers in our understanding of gene expression and…
Circular RNA discovery with emerging sequencing and deep learning technologies
Circular RNAs (circRNAs) are a unique type of RNA molecule that form a closed loop,…
CoupleVAE – coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data
As single-cell RNA sequencing becomes more advanced, scientists are now able to analyze the genetic…
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…
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…
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…
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…