scCIPHER - Contextual Deep Learning on Single-Cell Knowledge Graphs for Precision Medicine in Neurological Disorders

Neurological disorders are the leading driver of global disability and cause 16.8% of global mortality. Unfortunately, most lack disease-modifying treatments or cures. To address disease complexity and heterogeneity in neurological disease, we developed scCIPHER, an AI approach for Contextually Informed Precision HEalthcaRe using deep learning on single-cell knowledge graphs.

We constructed the Neurological Disease Knowledge Graph (NeuroKG), a neurobiological knowledge graph with 132K nodes and 3.98 million edges, by integrating 20 high-quality primary data sources with single-cell RNA-sequencing data from 3.37 million cells across 106 regions of the adult human brain. Next, we pre-trained a heterogeneous graph transformer on NeuroKG to create scCIPHER.

We leverage scCIPHER to make precision medicine-based predictions in neurological disorders across patient phenotyping, therapeutic response prediction, and causal gene discovery tasks, with validation in large-scale patient cohorts.


This is an ongoing research project.

Code Availability

Pytorch implementation of scCIPHER is available in the GitHub repository.


We are grateful to our collaborators, including Noa Dagan (Clalit Research Institute), Valentina Giunchiglia (Harvard Medical School), the Khurana Laboratory (Brigham and Women’s Hospital), and the Church Laboratory (Wyss Institute for Biologically Inspired Engineering).

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Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics