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Related Concept Videos

Machines01:19

Machines

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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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Machines: Problem Solving II01:30

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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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Proteomics01:33

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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.
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Machines: Problem Solving I01:22

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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.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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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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ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.

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
Summary
This summary is machine-generated.

ProteoBoostR is a new tool that helps researchers use machine learning (ML) on proteomic data for disease classification without coding. This application accelerates the discovery of protein biomarkers for clinical use.

Keywords:
Classification modelsMachine learningPersonalized medicineProteomicsXGBoost

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Area of Science:

  • Biomedical research
  • Proteomics
  • Machine learning

Background:

  • Mass spectrometry proteomics generates large datasets for biomarker discovery.
  • Biomedical researchers often lack the machine learning expertise to analyze complex proteomic data.
  • User-friendly tools are needed to apply advanced machine learning algorithms to proteomics.

Purpose of the Study:

  • To develop an accessible tool for applying machine learning to proteomics data.
  • To enable researchers without coding skills to perform advanced classification analyses.

Main Methods:

  • Developed ProteoBoostR, a Shiny application for supervised machine learning on protein abundance data.
  • ProteoBoostR provides an interactive web interface for training, evaluating, and applying XGBoost classification models.
  • The application requires no coding expertise from the user.

Main Results:

  • Demonstrated ProteoBoostR for classifying proteomic subtypes in glioblastoma multiforme.
  • Showcased its use in detecting lung adenocarcinoma from serum proteomic data.
  • Highlighted the application's ability to stratify patients using proteomic patterns.

Conclusions:

  • ProteoBoostR is an open-source application enabling proteomics researchers to perform advanced machine learning classification.
  • The tool facilitates reproducible machine learning analyses in proteomics.
  • It accelerates the translation of omics-based classifiers into clinical research.