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Related Concept Videos

Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
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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. 
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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.
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When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
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Isolation and Propagation of Circulating Tumor Cells from a Mouse Cancer Model
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Classifying tumors by supervised network propagation.

Wei Zhang1, Jianzhu Ma1, Trey Ideker1,2

  • 1Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

Bioinformatics (Oxford, England)
|June 29, 2018
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Summary
This summary is machine-generated.

Network-Based Supervised Stratification (NBS2) improves cancer subtype identification by learning crucial mutated subnetworks. This method accurately classifies new tumor mutation profiles, outperforming existing network-based approaches.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Genomics

Background:

  • Network propagation is used to analyze tumor mutations via molecular interaction networks.
  • Irrelevant interactions can obscure cancer-specific pathway signals and hinder subtype identification.

Purpose of the Study:

  • Introduce Network-Based Supervised Stratification (NBS2), a supervised algorithm for learning cancer-driving mutated subnetworks.
  • Improve the accuracy of tumor subtype classification using molecular network data.

Main Methods:

  • NBS2 trains by adjusting weights on molecular network interactions to best recover predefined tumor subtypes from reference mutation profiles.
  • The trained model classifies new tumor mutation profiles with fixed weights.

Main Results:

  • NBS2 demonstrates superior performance in classifying breast and glioblastoma tumors into known subtypes compared to existing network-based methods.
  • The algorithm successfully identifies characteristic molecular pathways driving specific cancer subtypes through weight interpretation.

Conclusions:

  • NBS2 offers a robust and accurate method for cancer subtype classification.
  • The approach enhances the interpretability of molecular pathways involved in tumorigenesis.