Opening the Black Box: Interpretable Machine Learning for Geneticists.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Machine learning (ML) excels at identifying complex patterns in high-dimensional genetic and genomic data.
• The inherent complexity of ML models often hinders interpretability, limiting biological insight generation.
• Interpretable ML approaches are crucial for understanding these complex models.

Purpose of the Study:

• To highlight the significance of interpretable ML in genetics and genomics.
• To review various strategies for interpreting ML models.
• To provide examples of these strategies in action.

Main Methods:

• Discussion of the importance of interpretable ML.
• Categorization and explanation of different ML interpretation strategies.
• Presentation of case studies and examples.

Main Results:

• Interpretable ML enhances the ability to derive novel biological insights from complex datasets.
• Various strategies exist for making ML models understandable to humans.
• Successful applications demonstrate the utility of interpretable ML in biological research.

Conclusions:

• Interpretable ML is vital for advancing genetics and genomics research.
• Continued development of interpretation methods is needed.
• Future directions include addressing challenges in applying interpretable ML to biological data.