関連する実験動画

Updated: May 1, 2026

A Human Glioblastoma Organotypic Slice Culture Model for Study of Tumor Cell Migration and Patient-specific Effects of Anti-Invasive Drugs
08:35

A Human Glioblastoma Organotypic Slice Culture Model for Study of Tumor Cell Migration and Patient-specific Effects of Anti-Invasive Drugs

Published on: July 20, 2017

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グリオマサブタイプ分類と生存予測のための説明可能な機械学習モデル

Olga Vershinina1,2, Victoria Turubanova1,2,3, Mikhail Krivonosov1,2

  • 1Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.

Cancers
|August 28, 2025
PubMed
まとめ

説明可能な機械学習モデルは,RNA-seqデータを用いてグリオマのサブタイプを正確に分類し,患者の生存率を予測します. 特定された重要な遺伝子は,改善された臨床意思決定のための腫瘍生物学と予後に関する洞察を提供します.

キーワード:
説明可能な人工知能遺伝子発現データグリオマ機械学習全体の生存予測サブタイプ分類

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