C-LJJAsingle-cell multiomics, AI, aging diagnostics
0-100 chain-logic scale · 15 dimensions · scored on public evidence
Develop an aging drug platform for precision drug optimisation at the individual level by combining somatic genotype with cellular phenotype at single-cell resolution.
Single-cell multiomics platform using machine learning and AI to integrate somatic genotype and cellular phenotype data.
Company site describes the platform as being built and highlights its application to aging diagnostics and therapeutics tools.
Create a human aging timer based on somatic evolution and cell lineage tree structure to estimate biological aging and support assessment of geroprotective trials.
Detect somatic mutations from single-cell RNA sequencing, infer cell lineage trees, and model chronological age using phylogenetic tree metrics.
GeroScience publication reported a cell tree age model using 31 tree metrics and validation on a public scRNA-seq dataset.
Reported Pearson correlation of 0.81 with chronological age and median absolute error of about 4 years; validation on public scRNA-seq data yielded Pearson correlation of 0.85. Predictions were reported to better predict some clinical biomarkers than chronological age alone.