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Related Concept Videos

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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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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How Data are Classified: Categorical Data01:11

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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.
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Classifying Matter by Composition03:35

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — 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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data.

Prasanna Vasudevan1, Thangamani Murugesan2

  • 11 Anna University, Chennai, Tamilnadu, India.

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|August 11, 2018
PubMed
Summary

This study identifies glioblastoma subtypes using graph clustering and a prognosis-enhanced neural network classifier. The methods achieved high accuracy in classifying cancer subtypes from genomic data.

Keywords:
cancer subtypesclassifiergenome scale datagraph clusteringmultidimensional dataprognosis-enhanced neural networksparse reduced-rank regression

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Large-scale cancer omics aims to uncover molecular mechanisms and identify therapeutic targets.
  • Understanding cancer subtypes is crucial for developing effective treatments.

Purpose of the Study:

  • To identify cancer subtypes from genome-scale data using clustering and classification.
  • To measure the accuracy of identified cancer subtypes.

Main Methods:

  • Max-flow/min-cut graph clustering was used for initial subtype recognition.
  • A prognosis-enhanced neural network classifier was developed for classification.
  • Analysis of microRNA expression data from 215 glioblastoma multiforme samples.

Main Results:

  • Max-flow/min-cut clustering achieved 88.93% accuracy.
  • The prognosis-enhanced neural network classifier demonstrated 89.2% accuracy.
  • Glioblastoma samples were classified into four subtypes: mesenchymal, classical, proneural, and neural.

Conclusions:

  • The prognosis-enhanced neural network classifier shows promise as an alternative method for cancer subtype prediction.
  • Accurate cancer subtype identification from genomic data is feasible.
  • This approach aids in understanding cancer heterogeneity and discovering novel biomedical targets.