{"paper":{"title":"Another look at predicting molecular breast cancer subtypes from the METABRIC data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Isabella Lazarov, Robert Tibshirani","submitted_at":"2026-07-23T20:57:13Z","abstract_excerpt":"Classifying patients into different clusters based on genomic data can offer valuable insights into their projected disease-specific survival trajectories over time. Here we apply two supervised learning methods---Nearest Shrunken Centroids and LASSO--- to the METABRIC Breast Cancer data {metabric} of 1980 patients and 754 genes to perform this task. The {pamr} R package implements the Nearest Shrunken Centroids classifier and the { glmnet} R package is used to fit an ungrouped multinomial model, a grouped multinomial model, and a One-Versus-Rest model. Splitting our data into discovery and va"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.21814","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.21814/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}