Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia.

ABSTRACT: Acute myeloid leukemia (AML) is a highly aggressive and heterogeneous disease, underscoring the need for improved therapeutic options and methods to optimally predict responses. With the wealth of available data resources, including clinical features, multiomics analysis, and ex vivo drug screening from AML patients, development of drug response prediction models has become feasible. Knowledge graphs (KGs) embed the relationships between different entities or features, allowing for explanation of a wide breadth of drug sensitivity and resistance mechanisms. We designed AML drug response prediction models guided by KGs. Our models included engineered features, relative gene expression between marker genes for each drug and regulators (e.g., transcription factors). We identified relat

SUBMITTER: Qin G 

PROVIDER: S-EPMC11401200 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature