A crucial part of the cis-regulatory code is the arrangement of transcription factor binding motifs. But, identifying the critical bases that alter their regulatory information remains a major challenge. Avsec et. al. developed interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. BPNet is a novel CNN that models the relationship between cis-regulatory sequence and TF binding profiles at base-resolution. It consists of multiple dilated convolutional layers with residual skip connections to learn increasingly complex predictive sequence patterns across the 1 kb sequence. This model is trained on ChIP-nexus profiles with high predictive performance. It uses DNA sequence to predict base-resolution ChIP-nexus binding profiles of pluripotency TFs. BPNet represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.
Input variables : DNA Sequence
Output Variables : Base-resolution ChIP-nexus binding profiles of pluripotency TFs
| 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 : kipoi.org
Additional links : biorxiv.org
| Model Category | : | Public |
| Date Published | : | February, 2021 |
| Healthcare Domain | : | Life Sciences |
| Code | : | kipoi.org |
| Health Risk Management |
| Health Risk Prediction |