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関連する概念動画

Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
1.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

587
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
587
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359
Survival Tree01:19

Survival Tree

499
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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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
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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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08:35

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Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
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科学分野:

  • 腫瘍学
  • バイオ情報学
  • コンピュータ生物学

背景:

  • グリオマは 予後が悪い 侵襲的な脳腫瘍で 早期に正確な診断が必要です
  • 腫瘍の分類と生存率の予測は,効果的な膠原腫治療戦略にとって極めて重要です.

研究 の 目的:

  • グリオマのサブタイプ (アストロサイトマ,オリゴデンドログリオマ,グリオブラストマ) を分類するための説明可能な機械学習 (ML) モデルを開発し,検証する.
  • RNA-sequencing (RNA-seq) データを用いて患者の生存率を予測する.
  • シェープリー添加式説明 (SHAP) 解析を通じてモデルの透明性を高める.

主な方法:

  • 公開されているRNA-seqデータセットの分析.
  • 重要な遺伝子バイオマーカーを特定するための特性の選択の適用.
  • 分類と生存分析のための様々なMLモデルの開発と比較
  • SHAP値を用いたモデル予測の解釈

主要な成果:

  • 13の重要な遺伝子 (例えばTERT,VEGFA,MMP9) は,グリオマのサブタイプと生存と有意に関連していることが確認された.
  • Support Vector Machine (SVM) は,0. 816のバランスのとれた精度と0. 896のAUCの分類を達成しました.
  • 症例対照コックス回帰 (CoxCC) モデルは,0. 809のC指数で強い生存予測を示した.
  • SHAP分析は,モデル結果に対する遺伝子発現の影響を洞察した.

結論:

  • 開発された説明可能なMLモデルは,膠原腫の診断と予後のための強力なツールを提供します.
  • これらのモデルは臨床医が患者の改善のための治療戦略を調整するのに役立ちます.
  • 特定された遺伝子バイオマーカーは,グリオマの病原性に関するさらなる研究の可能性を秘めています.