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関連する概念動画

Classifying Matter by Composition03:35

Classifying Matter by Composition

91.5K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
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Classifying Matter by State02:49

Classifying Matter by State

104.6K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
104.6K
Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.4K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.4K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.1K
Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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関連する実験動画

Updated: Feb 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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epiGPTope: 機械学習ベースのエピトープ生成・分類ツール

Natalia Flechas Manrique1, Alberto Martínez1, Elena López-Martínez2

  • 1Multiverse Computing, Parque Científico y Tecnológico de Gipuzkoa, Paseo de Miramón 170, Donostia-San Sebastián 20014, Spain.

ACS synthetic biology
|February 11, 2026
PubMed
まとめ
この要約は機械生成です。

研究者らは、免疫療法やワクチンのための新規エピトープ配列を生成する新しい大規模言語モデル、epiGPTopeを開発しました。このAIアプローチは、生物学的に実行可能な配列を作成することにより、合成エピトープの発見を加速します。

キーワード:
人工知能エピトープ分類子エピトープ生成大規模言語モデルライブラリ設計機械学習

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科学分野:

  • 免疫情報学
  • 計算生物学
  • バイオテクノロジーにおける人工知能

背景:

  • エピトープは、免疫療法、ワクチン、診断薬の開発に不可欠です。
  • 合成エピトープライブラリの設計は、広大な配列空間により、実験的スクリーニングが不可能であるという課題があります。

研究 の 目的:

  • 新規線状エピトープ配列生成のための大規模言語モデル、epiGPTopeを紹介します。
  • エピトープ発見とライブラリ設計を加速するための計算アプローチを開発します。

主な方法:

  • 線状エピトープデータで大規模言語モデル（epiGPTope）をファインチューニングします。
  • 類似した統計的特性を持つ新規エピトープ様配列を生成するために、生成モデルを利用します。
  • エピトープ配列の起源（細菌またはウイルス）を予測するために、統計的分類子をトレーニングします。

主要な成果:

  • epiGPTopeは、既知のエピトープに類似した統計的特性を持つ新規エピトープ様配列を生成することに成功しました。
  • 生成アプローチにより、候補エピトープライブラリの作成が可能になります。
  • 予測モデルは、細菌とウイルスのエピトープ起源を区別し、候補選択を洗練させることができます。

結論:

  • 生成モデルと予測モデルの組み合わせは、エピトープ発見のための強力なツールを提供します。
  • このAI駆動型アプローチは、複雑な構造データや手作りの特徴の必要性を回避します。
  • このアプローチは、バイオテクノロジー用途向けの合成エピトープの、より迅速で費用対効果の高い生成とスクリーニングを約束します。