Integration of artificial intelligence and high-content screening enabled identification of drugs for long-term treatment of cerebral cavernous malformation disease.

ABSTRACT:

Background

Adults and children with cerebral cavernous malformations (CCMs) are at risk of experiencing lifelong complications such as hemorrhagic strokes, neurological deficits, and epileptic seizures. These complications can severely reduce quality of life. At present, there is no safe or effective therapeutic option for the long-term treatment of CCMs.

Methods

Using advanced artificial intelligence (AI) and machine learning models, powered by the Benevolent Platform^™, we aimed to identify therapeutic drug targets for CCM pathology (e.g., CCM1, CCM2, CCM3). An AI integrative approach utilized various data types from biomedical entities, including diseases, genes, tissues, and biological mechanisms, together with CCM transcriptomic experimental data. High-through

SUBMITTER: Frias-Anaya E 

PROVIDER: S-EPMC12712899 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature