Nonparametric IPSS: fast, flexible feature selection with false discovery control
Abstract
Domain fit: Niche / domain-specific · No strong AI-core implementation/artifact signals were detected from current providers.
All code and data used in this work are available on GitHub (https://github.com/omelikechi/ipss_bioinformatics) and permanently archived on Zenodo (https://doi.org/10.5281/zenodo.15335289). A Python package for implementing IPSS is available on GitHub (https://github.com/omelikechi/ipss) and PyPI (https://pypi.org/project/ipss/). An R implementation of IPSS is also available on GitHub (https://github.com/omelikechi/ipssR).
Results and benchmarks
All code and data used in this work are available on GitHub (https://github.com/omelikechi/ipss_bioinformatics) and permanently archived on Zenodo (https://doi.org/10.5281/zenodo.15335289).
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: 29 False positives and false negatives in genome scans.
- Track assumptions and missing details in an experiment log before coding.
Time to first repro: a few days
Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.
- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
No verified implementation available
- No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
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Research context
0
Citations
30
References
Tasks
Computer science, Nonparametric statistics, False positives and false negatives, False positive paradox, Feature selection, False discovery rate, Python (programming language), Source code
Methods
None detected
Domains
Artificial intelligence, Machine learning, Mathematics
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