---
title: "Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.00477v1"
permalink: "https://feed.astralor.com/items/itm_ef0c557e40d7d8a1be794051"
published: "2026-10-31T00:05:52.000Z"
score: 55
channel: "ai"
tags: ["Ensemble Classification","Cancer-Specific Gene Signatures","Quality-Aware Hybrid Gene","Selection","TCGA RNA-Seq","Gene-expression","Starting","Boruta"]
---

# Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data

## 速览

Gene-expression classifiers of cancer type are usually developed and validated within one cohort using random splits. Starting from a quality-aware hybrid Boruta selector from earlier work, we developed and evaluated en…

## 关键点

- Gene-expression classifiers of cancer type are usually developed and validated within one cohort using random splits.
- Starting from a quality-aware hybrid Boruta selector from earlier work, we developed and evaluated ensemble classifiers and cancer-specific gene signatures.
- Using 10,469 TCGA tumours of 33 types, we compared five learners and three ensembles under decision rules fixed in advance, reran the selection in site-grouped cross-validation, transferred a breast-cancer subtype panel…

## 全文

Gene-expression classifiers of cancer type are usually developed and validated within one cohort using random splits. Starting from a quality-aware hybrid Boruta selector from earlier work, we developed and evaluated ensemble classifiers and cancer-specific gene signatures. Using 10,469 TCGA tumours of 33 types, we compared five learners and three ensembles under decision rules fixed in advance, reran the selection in site-grouped cross-validation, transferred a breast-cancer subtype panel to 3273 tumours of the independent SCAN-B cohort, and built five-gene stage~I tumour versus adjacent-normal panels for nine cancer types. Logistic regression (LR) was the most accurate and best-calibrated single learner (balanced accuracy 0.943 on the locked test set). Stacking added at most 0.006 with 1000 genes but improved on LR in every fold with a minimal 100-gene panel; a hierarchical model did not reduce within-group confusion. Site-grouped validation lowered balanced accuracy by 0.027, almost entirely in colon and rectal cancer (0.008 without them and the untestable LAML). On SCAN-B, the subtype panel reached 0.855, close to the 50 PAM50 genes (0.876). Stage~I panels reached ROC AUC >= 0.99 in held-out patients and detected most later-stage tumours. Ensembles help most with a minimal panel; with 1000 genes, a calibrated linear model suffices.

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