bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning
bioMoR enhances genomic learning efficiency via biology-guided recursion mixture, boosting macro-F1 by 8.2%.
Key Findings
Methodology
bioMoR framework integrates biological knowledge with Mixture-of-Recursions architecture, first applied to gene and pathway learning. Graph-based information sharing refines token embeddings, structural bias guides self-attention, and graph-aware router determines recursion depth.
Key Results
- bioMoR improves average macro-F1 by 8.2 percentage points and balanced accuracy by 7.1 percentage points across eight benchmarks, using 75% fewer parameters and 58% fewer FLOPs.
- Biologically relevant pathways are selected for deeper computation, confirming model's biological interpretability.
- Ablation study shows embedding injection contributes most to performance improvement.
Significance
bioMoR significantly enhances efficiency and accuracy in genomic learning, addressing the computational resource wastage of traditional models when handling high-dimensional data. Its biological interpretability aids in understanding gene and pathway roles.
Technical Contribution
bioMoR provides new theoretical guarantees and engineering possibilities by guiding recursion depth selection with biological knowledge, differing from existing recursive models, especially in biological relevance.
Novelty
bioMoR is the first framework to combine biological knowledge with Mixture-of-Recursions architecture, overcoming the bottleneck of traditional models' inability to effectively utilize biological information.
Limitations
- In certain datasets, biological knowledge injection may lead to overfitting.
- Model performance is limited when handling extremely imbalanced data.
- Further validation in other domains is needed.
Future Work
Future research can explore bioMoR's application to other biological datasets and optimize biological knowledge injection mechanisms to enhance model robustness.
AI Executive Summary
In the field of genomics, existing transformer models often face the problem of computational resource wastage when handling high-dimensional data. The bioMoR framework, by integrating biological knowledge with Mixture-of-Recursions architecture, is first applied to gene and pathway learning, significantly improving model efficiency and accuracy.
bioMoR uses graph-based information sharing to refine token embeddings, structural bias to guide self-attention, and a graph-aware router to determine recursion depth. Experimental results show that bioMoR improves average macro-F1 by 8.2 percentage points and balanced accuracy by 7.1 percentage points across eight benchmarks, using 75% fewer parameters and 58% fewer FLOPs.
Despite bioMoR's outstanding performance in biological interpretability and computational efficiency, biological knowledge injection may lead to overfitting in certain datasets. Future research can explore bioMoR's application to other biological datasets and optimize biological knowledge injection mechanisms to enhance model robustness.
Deep Analysis
Background
The field of genomics has seen significant progress recently, especially in single-cell sequencing and multi-omics data analysis. Traditional transformer models like scBERT and Geneformer excel in gene representation learning but often face computational resource wastage when handling high-dimensional data.
Core Problem
Existing transformer models struggle to effectively select marker genes or pathways that require deep computation, leading to computational resource wastage. Improving computational efficiency while maintaining accuracy is a pressing issue.
Innovation
The bioMoR framework dynamically adjusts marker gene and pathway selection and recursion depth by integrating biological knowledge with Mixture-of-Recursions architecture. Its innovation lies in using biological knowledge to guide the model's computation process, rather than just as a static prior.
Methodology
- �� Utilize graph-based information sharing to refine token embeddings.
- �� Structural bias guides self-attention, promoting information exchange between biologically related tokens.
- �� Graph-aware router determines recursion depth based on biological neighborhood information.
Experiments
Experimental design includes eight benchmark datasets covering single-cell and multi-omics data. Performance is evaluated using five-fold cross-validation, comparing bioMoR with traditional transformer models and recursive models.
Results
bioMoR improves average macro-F1 by 8.2 percentage points and balanced accuracy by 7.1 percentage points across eight benchmarks, using 75% fewer parameters and 58% fewer FLOPs. Biologically relevant pathways are selected for deeper computation, confirming model's biological interpretability.
Applications
bioMoR can be directly applied to genomic data analysis, especially in scenarios requiring efficient handling of high-dimensional data. Its biological interpretability aids in understanding gene and pathway roles.
Limitations & Outlook
Despite bioMoR's outstanding performance in biological interpretability and computational efficiency, biological knowledge injection may lead to overfitting in certain datasets. Future research can explore bioMoR's application to other biological datasets and optimize biological knowledge injection mechanisms to enhance model robustness.
Plain Language Accessible to non-experts
Imagine a factory where workers need to process a large amount of raw materials, but only a portion is high quality. bioMoR acts like a smart manager who can identify which materials need more processing time, rather than wasting resources on low-quality ones. This way, the factory can produce high-quality products more efficiently.
ELI14 Explained like you're 14
Hey, friends! Imagine you're playing a game with many levels, but only a few are really hard. bioMoR is like a super helper that finds those tough levels and tells you how to beat them with the least time and effort. This way, you can win the game faster and easier!
Glossary
Transformer
A deep learning model for processing sequence data, excels at capturing long-range dependencies.
Used for genomic data representation learning in this paper.
Mixture-of-Recursions
A method to improve computational efficiency by dynamically selecting token recursion depth.
Core mechanism of the bioMoR framework.
Graph-based Information Sharing
A method to optimize token embeddings through graph structures.
Used to refine token embeddings in bioMoR.
Self-attention
A method to optimize representations by calculating relationships between elements in the input sequence.
Promotes information exchange between biologically related tokens in bioMoR.
FLOPs
A measure of model computational complexity, representing the number of floating-point operations performed.
Used to evaluate bioMoR's computational efficiency.
Open Questions Unanswered questions from this research
- 1 How to optimize bioMoR's performance on extremely imbalanced data?
- 2 How does biological knowledge injection affect model generalization ability?
Applications
Immediate Applications
Genomic Data Analysis
bioMoR can enhance the efficiency and accuracy of genomic data analysis, suitable for researchers and bioinformaticians.
Long-term Vision
Precision Medicine
By optimizing genomic data analysis, bioMoR has the potential to advance precision medicine and improve patient treatment outcomes.
Abstract
Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation. Mixture-of-Recursions (MoR) improves efficiency through adaptive token-choice or expert-choice routing. We propose bioMoR, which, to the best of our knowledge, is the first framework to apply MoR to gene-level and pathway-level learning. Our contributions include identifying three locations for integrating structured biological knowledge within an MoR backbone: graph-based information sharing refines token embeddings, a structural bias guides self-attention toward biologically related tokens, and a graph-aware router uses neighborhood information to determine each token's recursion depth. These techniques are centered on our insight that additional knowledge of token interaction can effectively help models construct embeddings and select which tokens should be learned more deeply. Across eight benchmarks spanning diverse omics data types and evaluated under a unified five-fold cross-validation protocol, bioMoR improves average macro-F1 by 8.2 percentage points and balanced accuracy by 7.1 percentage points over the strongest biology-agnostic MoR baseline while using 75 percent fewer parameters and up to 58 percent fewer FLOPs than a non-recursive Transformer. The selected marker genes or pathways provide biological interpretability, while their token-specific recursion depths reveal how computation is allocated.