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Area of Science:

  • Computational biology
  • Systems biology
  • Artificial intelligence in medicine

Background:

  • Computational models of cellular networks are crucial for understanding disease mechanisms and developing therapies.
  • Biology-informed neural networks (BINNs) integrate deep learning with prior biological knowledge to build these models.
  • A key challenge is validating the reliability of inferred mechanisms due to cellular complexity and unknown interactions.

Purpose of the Study:

  • To develop and evaluate a holistic approach for assessing the reliability of mechanisms inferred by BINNs.
  • To introduce and quantify the 'self-pruning' of spurious interactions by BINNs during training as a reliability metric.
  • To enhance the computational efficiency of existing BINN frameworks for large-scale analysis.

Main Methods:

  • Implemented a GPU-accelerated version of LEMBAS (Large-scale knowledge-EMBedded Artificial Signaling-networks) for intracellular signaling dynamics, achieving a >7-fold speedup.
  • Introduced purposefully spurious interactions into prior knowledge networks (PKNs) and measured their removal (self-pruning) by the BINN during training.
  • Evaluated the self-pruning metric across three diverse datasets, applying L2 regularization to the model.

Main Results:

  • The GPU-accelerated LEMBAS implementation maintained predictive accuracy while significantly improving computational speed.
  • BINNs demonstrated a greater extent of self-pruning for randomly introduced spurious interactions compared to those present in the PKN.
  • The self-pruning metric proved scalable, generalizable, and independent of manual curation, applicable across different network settings.

Conclusions:

  • Self-pruning serves as a quantitative indicator of BINN robustness to uncertainty in prior biological knowledge.
  • This metric suggests that BINNs can effectively model real biological systems by distinguishing true interactions from noise.
  • The enhanced LEMBAS framework and self-pruning metric offer a powerful, efficient tool for advancing computational systems biology.