Expanding P-NET, a multi-purpose biologically informed deep learning framework
Elmarakeby, H.; Glettig, M.; Zhou, A.; Zhou, C.; Tarantino, G.; Aprati, T.; Van Allen, E.; Liu, D.
Show abstract
We present expanded P-NET, a versatile frame-work for deep learning in computational biology based on P-NET, leveraging biological pathways for interpretable predictions. Our framework achieves competitive performance in genomic & transcriptomic prediction tasks. We demonstrate its stability and interpretability compared to traditional machine learning models. P-NET 2.0 incorporates gene and pathway information, providing valuable insights into complex biological processes. The framework is publicly available, enabling its application to various computational biology tasks.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.