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Predictive model for gRNA (guide RNA) editing efficiency in gene editing, building on GNL scorer methodology for CRISPR-like applications
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This is a 4-star, 0-fork repository with zero velocity over 1537 days—a stagnant personal project. The README context is minimal, suggesting it's an academic exercise or abandoned work-in-progress. The core contribution is an 'expansion on GNL scorer,' which positions it as an incremental refinement of existing CRISPR efficiency prediction methods rather than a novel approach. The biotech/ML space for gRNA design is now crowded: Anthropic's EvoDiff, OpenAI partnerships with biotech, and specialized tools like Deep-CRISPR, CIRCLE-seq, and commercial solutions (Synthego, Benchling) have effectively commoditized this capability. Frontier labs (OpenAI, Anthropic, Google DeepMind) are actively building foundation models for protein/DNA design and have resources to absorb or surpass this work trivially. No evidence of adoption, community, or production usage. This is a dead tutorial-grade project with no defensibility—the pattern (LM + CRISPR scoring) is well-trodden, and better-funded actors control the space.
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