BABEL is a deep learning model written in Python designed to translate between mutliple single cell modalities. It is designed to translate between scATAC-seq and scRNA-seq profiles. It does so by learning encoder networks that can project these two modalities into a shared latent representation, and decoder networks that can take this representation and reconstruct expression or chromatin accessibility profiles. This model makes it possible to computationally synthesize matched multiomic measurements when only one modality is experimentally available. It complements experimental advances to efficiently achieve single-cell multiomic insight.
Input variables : scATAC-seq, scRNA-seq
Output Variables : chromatin accessibility profiles, single-cell expression
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : pnas.org
| Model Category | : | Public |
| Date Published | : | November, 2020 |
| Healthcare Domain | : | Life Sciences |
| Code | : | Not available |
| Health Risk Management |
| Health Risk Prediction |