A new AI tool developed by UC San Diego researchers uses clinical data to predict colorectal cancer risk in ulcerative colitis patients, helping clinicians make evidence‑based care decisions and preve...

Back up code to structure pathology reports. Repository was originally a clone of llama.cpp, then modified for data structuring - bdj34/llama.cpp_data_extraction

Objective Accurate data resources are essential for impactful medical research, but available structured datasets are often incomplete or inaccurate. Recent advances in open-weight large language mode...

Background The risk of developing advanced neoplasia (AN; colorectal cancer and/or high-grade dysplasia) in ulcerative colitis (UC) patients with a low-grade dysplasia (LGD) lesion is variable and dif...

Here, the authors apply novel pangenome indexes created from diverse human references to facilitate computationally efficient removal of host genetic information, preserving biological accuracy and pr...

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insuff...

Background & Aims Accurate data resources are essential for impactful medical research. To date, most large-scale studies have relied on structured sources, such as International Classification of Dis...