Nonparametric IPSS: fast, flexible feature selection with false discovery control.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40358526", "kind": "science-record", "revision": 3, "content_hash": "sha256:cbc0ffc832f87d4091801c9dd78f3f578987a3b219db35a6124b9bede71ce459", "created_at": "2026-11-01T20:30:31Z", "updated_at": "2026-11-01T22:14:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Nonparametric IPSS: fast, flexible feature selection with false discovery control.", "doi": "10.1093/bioinformatics/btaf299", "pmid": "40358526", "authors": ["Omar Melikechi", "David B Dunson", "Jeffrey W Miller"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or generalized linear models, (ii) lack theoretical false discovery control, or (iii) identify few true positives.\nRESULTS: We introduce a general feature selection method with finite-sample false discovery control based on applying integrated path stability selection (IPSS) to arbitrary feature importance scores. The method is nonparametric whenever the importance scores are nonparametric, and it estimates q-values, which are better suited to high-dimensional data than P-values. We focus on two special cases using importance scores from gradient boosting (IPSSGB) and random forests (IPSSRF). Extensive nonlinear simulations with RNA sequencing data show that both methods accurately control the false discovery rate and detect more true positives than existing methods. Both methods are also efficient, running in under 20 s when there are 500 samples and 5000 features. We apply IPSSGB and IPSSRF to detect microRNAs and genes related to cancer, finding that they yield better predictions with fewer features than existing approaches.\nAVAILABILITY AND IMPLEMENTATION: 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).", "claims": [{"id": "claim-method", "statement": "We focus on two special cases using importance scores from gradient boosting (IPSSGB) and random forests (IPSSRF).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40358526", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We focus on two special cases using importance scores from gradient boosting (IPSSGB) and random forests (IPSSRF)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "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).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40358526", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "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)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "IPSSGB identified 70% of important features at a target FDR of 0.1, compared with 40% for IPSSRF and close to none for the remaining methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40358526", "locator": "sections[14] \"3.4 Simulation results\" paragraph 6", "quote": "For example, in the regression results in Fig. 1, IPSSGB identifies 70% of important features when the target FDR is 0.1, while IPSSRF identifies 40% and the remaining methods identify close to none."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "IPSSGB identified 70% of important features at a target FDR of 0.1, compared with 40% for IPSSRF and close to none for the remaining methods.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40358526"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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