# Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction

arXiv: https://arxiv.org/abs/2510.00512 · HTML: https://mlsanity.com/paper/2510.00512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Abductive Knowledge Induction From Raw Data](https://mlsanity.com/paper/2010.03514/) (2020)
- [Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all](https://mlsanity.com/paper/2410.13956/) (2024)
- [Boolean Matrix Factorization and Noisy Completion via Message Passing](https://mlsanity.com/paper/1509.08535/) (2015)
- [Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection](https://mlsanity.com/paper/2412.08457/) (2024)
- [TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction](https://mlsanity.com/paper/2505.14919/) (2025)
- [Boolean Matrix Logic Programming on the GPU](https://mlsanity.com/paper/2408.10369/) (2024)
- [Automated Reasoning in Systems Biology: a Necessity for Precision Medicine](https://mlsanity.com/paper/2410.13487/) (2024)

## Abstract (arXiv)

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. To address this challenge, we propose ALIGNED (Adaptive aLignment for Inconsistent Genetic kNowledgE and Data), a neuro-symbolic framework based on the Abductive Learning (ABL) paradigm. This end-to-end framework aligns neural and symbolic components and performs systematic knowledge refinement. We introduce a balanced consistency metric to evaluate the predictions' consistency against both data and knowledge. Our results show that ALIGNED outperforms state-of-the-art methods by achieving the highest balanced consistency, while also re-discovering biologically meaningful knowledge. Our work advances beyond existing methods to enable both the transparency and the evolution of mechanistic biological understanding.