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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...
91.5K
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...
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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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Video Experimental Relacionado

Updated: Feb 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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epiGPTope: Un generador y clasificador de epítopos basado en el aprendizaje automático.

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
Resumen
Este resumen es generado por máquina.

Los investigadores desarrollaron epiGPTope, un nuevo modelo de lenguaje grande, para generar nuevas secuencias de epítopos para inmunoterapias y vacunas. Este enfoque de IA acelera el descubrimiento de epítopos sintéticos mediante la creación de secuencias biológicamente factibles.

Palabras clave:
La inteligencia artificial es la inteligencia artificial.Los clasificadores de epítopos son clasificadores de epítopos.La generación de epítopos generación de epítopos.Modelo de lenguaje de gran tamaño.Diseño de biblioteca diseño de biblioteca diseño de biblioteca.Aprendizaje automático de aprendizaje automático.

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Área de la Ciencia:

  • La inmunoinformática es una ciencia de la inmunología.
  • Biología computacional Biología computacional.
  • La inteligencia artificial en la biotecnología.

Sus antecedentes:

  • Los epítopos son críticos para el desarrollo de inmunoterapias, vacunas y diagnósticos.
  • El diseño de bibliotecas de epítopos sintéticos se ve obstaculizado por el vasto espacio de secuencia, lo que hace que la detección experimental sea inviable.

Objetivo del estudio:

  • Para presentar epiGPTope, un gran modelo de lenguaje para generar nuevas secuencias de epítopos lineales.
  • Desarrollar un enfoque computacional para acelerar el descubrimiento de epítopos y el diseño de bibliotecas.

Principales métodos:

  • Ajuste fino de un modelo de lenguaje grande (epiGPTope) en datos de epitope lineal.
  • Utilizando modelos generativos para producir nuevas secuencias similares a epítopos con propiedades estadísticas análogas.
  • Capacitación de clasificadores estadísticos para predecir el origen (bacteriano o viral) de las secuencias de epítopos.

Principales resultados:

  • EpiGPTope genera con éxito nuevas secuencias similares a epítopos con propiedades estadísticas similares a las de los epítopos conocidos.
  • El enfoque generativo permite la creación de bibliotecas de epítopos candidatos.
  • Los modelos predictivos pueden diferenciar entre los orígenes de epítopos bacterianos y virales, refinando la selección de candidatos.

Conclusiones:

  • La combinación de modelos generativos y predictivos ofrece una poderosa herramienta para el descubrimiento de epítopos.
  • Este método impulsado por la IA evita la necesidad de datos estructurales complejos o características hechas a mano.
  • El enfoque promete una generación más rápida y más rentable y la detección de epítopos sintéticos para aplicaciones biotecnológicas.