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Machines01:19

Machines

563
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...
563
Machines: Problem Solving II01:30

Machines: Problem Solving II

652
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.
652
Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
9.4K
Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
701
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
Predator-Prey Interactions02:39

Predator-Prey Interactions

21.2K
Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Video Experimental Relacionado

Updated: Jan 26, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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ProteoBoostR: un marco interactivo para el aprendizaje automático supervisado en proteómica clínica

Annika Topitsch1,2,3,4, Niko Pinter1, Tilman Werner1

  • 1Institute for Surgical Pathology, Medical Center, Medical Faculty, University of Freiburg, University of Freiburg, 79106, Freiburg, Germany.

Clinical proteomics
|January 24, 2026
PubMed
Resumen
Este resumen es generado por máquina.

ProteoBoostR es una nueva herramienta que ayuda a los investigadores a utilizar el aprendizaje automático (AA) en datos proteómicos para la clasificación de enfermedades sin necesidad de codificación. Esta aplicación acelera el descubrimiento de biomarcadores de proteínas para uso clínico.

Palabras clave:
modelos de clasificaciónaprendizaje automáticomedicina personalizadaproteómicaXGBoost

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Área de la Ciencia:

  • Investigación biomédica
  • Proteómica
  • Aprendizaje automático

Sus antecedentes:

  • La proteómica de espectrometría de masas genera grandes conjuntos de datos para el descubrimiento de biomarcadores.
  • Los investigadores biomédicos a menudo carecen de la experiencia en aprendizaje automático para analizar datos proteómicos complejos.
  • Se necesitan herramientas fáciles de usar para aplicar algoritmos avanzados de aprendizaje automático a la proteómica.

Objetivo del estudio:

  • Desarrollar una herramienta accesible para aplicar el aprendizaje automático a datos proteómicos.
  • Permitir a los investigadores sin habilidades de codificación realizar análisis de clasificación avanzados.

Principales métodos:

  • Se desarrolló ProteoBoostR, una aplicación Shiny para aprendizaje automático supervisado en datos de abundancia de proteínas.
  • ProteoBoostR proporciona una interfaz web interactiva para entrenar, evaluar y aplicar modelos de clasificación XGBoost.
  • La aplicación no requiere experiencia en codificación por parte del usuario.

Principales resultados:

  • Se demostró ProteoBoostR para clasificar subtipos proteómicos en glioblastoma multiforme.
  • Se mostró su uso en la detección de adenocarcinoma de pulmón a partir de datos proteómicos séricos.
  • Se destacó la capacidad de la aplicación para estratificar pacientes utilizando patrones proteómicos.

Conclusiones:

  • ProteoBoostR es una aplicación de código abierto que permite a los investigadores de proteómica realizar clasificaciones avanzadas de aprendizaje automático.
  • La herramienta facilita análisis de aprendizaje automático reproducibles en proteómica.
  • Acelera la traducción de clasificadores basados en ómicas en la investigación clínica.