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[2602.22551] A Fast and Practical Column Generation Approach for Identifying Carcinogenic Multi-Hit Gene Combinations
Summary
This paper presents a novel approach to identifying carcinogenic multi-hit gene combinations using a fast column generation method, significantly improving computational efficiency in cancer genomics research.
Why It Matters
Understanding the genetic basis of cancer is crucial for developing targeted therapies. This research offers a more efficient method for identifying gene combinations that drive cancer, potentially accelerating advancements in personalized medicine and cancer treatment strategies.
Key Takeaways
- Introduces a new approach to the Multi-Hit Cancer Driver Set Cover Problem (MHCDSCP).
- Achieves comparable performance to existing methods while running on standard hardware.
- Suggests that the problem is less computationally intensive than previously thought.
- Opens new research avenues for exploring modeling assumptions in cancer genomics.
- Highlights the importance of efficient algorithms in advancing cancer research.
Mathematics > Optimization and Control arXiv:2602.22551 (math) [Submitted on 26 Feb 2026] Title:A Fast and Practical Column Generation Approach for Identifying Carcinogenic Multi-Hit Gene Combinations Authors:Rick S. H. Willemsen, Tenindra Abeywickrama, Ramu Anandakrishnan View a PDF of the paper titled A Fast and Practical Column Generation Approach for Identifying Carcinogenic Multi-Hit Gene Combinations, by Rick S. H. Willemsen and 2 other authors View PDF HTML (experimental) Abstract:Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these combinations is critical for understanding carcinogenesis and designing targeted therapies. We formalise this challenge as the Multi-Hit Cancer Driver Set Cover Problem (MHCDSCP), a binary classification problem that selects gene combinations to maximise coverage of tumor samples while minimising coverage of normal samples. Existing approaches typically rely on exhaustive search and supercomputing infrastructure. In this paper, we present constraint programming and mixed integer programming formulations of the MHCDSCP. Evaluated on real-world cancer genomics data, our methods achieve performance comparable to state-of-the-art methods while running on a single commodity CPU in under a minute. Furthermore, we introduce a column generation heuristic capable of solving small instances to optimality. These results suggest that solving the MHCDSCP is less...