Single-cell genomics has powered recent advances in bioinformatics, leading to important discoveries in cell-cell relationships, disease, and epigenetics.
As single-cell, machine learning-driven bioinformatics datasets increase in size to millions of cells, though, the limitations of traditional analysis methods quickly become clear.
The emerging use of artificial intelligence (AI) and machine learning (ML) in bioinformatics is helping to address some of the challenges associated with processing minute details from large datasets and generating interactive results. But even the current ML bioinformatic processes are slow – and single-cell datasets continue to grow.
How can AI bioinformatic researchers accelerate the speed and quality of single-cell genomics analysis? Emily So, Application Scientist, Code Ocean, shares her perspective on GPU-accelerated workflows and the significant improvement they have shown toward fast and accurate single-cell genomic discovery in an informative on-demand webinar.
