Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction
Summary: arXiv:2510.00512v2 Announce Type: replace-cross
Abstract
The transcriptional response to genetic perturbation reveals fundamental insights into complex cellular systems. While current approaches have made progress in predicting genetic perturbation responses, they provide limited biological understanding and cannot systematically refine existing knowledge. Overcoming these limitations requires an end-to-end integration of data-driven learning and existing knowledge. However, this integration is challenging due to inconsistencies between data and knowledge bases, such as noise, misannotation, and incompleteness.
Introduction
In the rapidly evolving field of genomics, accurately predicting how genetic perturbations affect cellular behavior is essential for advancing our understanding of biological systems. Traditional methods have been limited in their ability to generalize findings and integrate new data with existing biological knowledge. To bridge this gap, researchers have proposed innovative frameworks that merge data-driven approaches with symbolic reasoning.
Challenges in Integration
One significant hurdle in achieving effective integration is the presence of inconsistencies between data and knowledge bases. These inconsistencies can manifest in various forms:
- Noise: Random errors in data collection can lead to misleading interpretations.
- Misannotation: Incorrect labeling of genetic data can result in inaccuracies in predictions.
- Incompleteness: Missing data points can impair the model’s ability to learn effectively.
The ALIGNED Framework
To tackle these integration challenges, we propose ALIGNED (Adaptive aLignment for Inconsistent Genetic kNowledgE and Data), a neuro-symbolic framework founded on the Abductive Learning (ABL) paradigm. This innovative framework aligns neural networks with symbolic reasoning to facilitate systematic knowledge refinement. By employing a balanced consistency metric, ALIGNED assesses the predictions’ alignment with both data and knowledge, ensuring a robust evaluation process.
Performance and Results
Our experimental results demonstrate that ALIGNED surpasses existing state-of-the-art methods by achieving the highest balanced consistency scores. Additionally, the framework successfully re-discovers biologically significant knowledge that was previously overlooked. This capability not only enhances prediction accuracy but also fosters greater transparency in the underlying biological mechanisms.
Conclusion
The ALIGNED framework marks a significant advancement in the field of genetic perturbation prediction. By integrating data-driven learning with existing biological knowledge, it addresses the critical limitations of traditional methods. This work not only enhances predictive accuracy but also contributes to the evolution of a more comprehensive understanding of complex biological systems. Future research will focus on refining the framework further and exploring its applications in various genomic contexts.