MonoNet: enhancing interpretability in neural networks via monotonic features

Area of Science:

• Computational biology
• Machine learning
• Bioinformatics

Background:

• Interpreting machine learning models is crucial, especially in high-stakes fields like medical informatics.
• A common challenge is the trade-off between model accuracy and interpretability.
• There is a growing need for transparent yet powerful AI models.

Purpose of the Study:

• Introduce MonoNet, a novel neural network designed for enhanced interpretability.
• Demonstrate MonoNet's ability to retain high learning capabilities comparable to traditional neural models.
• Showcase the utility of monotonic constraints for model interpretation.

Main Methods:

• Developed MonoNet, a structurally constrained neural network with monotonically connected layers.
• Ensured monotonic relationships between high-level features and model outputs.
• Applied post-hoc strategies in conjunction with monotonic constraints for model interpretation.

Main Results:

• MonoNet achieved high performance in classifying cellular populations in single-cell proteomic data.
• The model provided valuable biological insights into key biomarkers.
• Experiments on benchmark datasets across different domains confirmed MonoNet's capabilities.
• Information-theoretical analysis validated the contribution of monotonic constraints to the learning process.

Conclusions:

• MonoNet offers a transparent alternative to traditional neural networks without sacrificing performance.
• The model's interpretability facilitates understanding of complex biological datasets.
• MonoNet enhances trust in AI predictions within critical scientific applications.