Hi everyone,
I would like to share an open-source computational framework and formal specification designed to integrate biological sequences using a shared latent space.
The pipeline unifies advanced discrete derivatives, numerical integration, topological graph analysis (k-mers), and a late-fusion neural architecture with specialized encoders projected via a GELU activation into a shared latent space Z E RNxd .. P PY
The primary goal is to provide a robust, reproducible mathematical and computational base for structural efficacy prediction, keeping a strict separation between algorithmic modeling and empirical wet-lab validation. PY
Preprint (Zenodo DOI): https://zenodo.org/records/22135148
GitHub Repository: https://github.com/duirk/caracterizacion-computacional-adn
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I really wonder what people are thinking when "sharing" a "pipeline" like this. First, it is in spanish. Second, it is just a repository without any code examples or usage documentation. Third, it doesn't seem to discuss the output and doesn't provide any benchmarks against competitors. So what's the point? Is this AI slop?
Thank you for taking the time to review the repository. To clarify, the framework includes a complete, functional PyTorch pipeline (modelo_genomico_v1.0.2.py) along with a formal specification preprint detailing its architecture and mathematical foundations. The primary purpose of this release is to provide an experimental base designed for inserting and integrating custom input data from biological sequences. While the initial post was shared in Spanish, the core codebase and preprint are structured to serve as an exploratory computational framework for the scientific community. Because it is strictly an experimental framework, the current release establishes a rigorous ablation and evaluation protocol rather than a plug-and-play benchmark tool. Constructive contributions, usage examples, or suggestions to expand the comparative benchmarks are always welcome in the repository.