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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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Design Example: Setting a Curve Using Design Data01:09

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Secretory Phase01:19

Secretory Phase

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Classifying Matter by State02:49

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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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Related Experiment Video

Updated: Jan 21, 2026

Separation and Fractionation of Cell Wall and Cell Membrane Proteins from Mycobacterium tuberculosis for Downstream Protein Analysis
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SecProMTB: Support Vector Machine-Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to

Chaolu Meng1,2, Leyi Wei1, Quan Zou1,3,4

  • 1College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China.

Proteomics
|July 27, 2019
PubMed
Summary

This study introduces SecProMTB, a bioinformatics tool for identifying Mycobacterium tuberculosis secretory proteins. The advanced support vector machine model achieves 86% accuracy, offering a faster alternative to traditional methods.

Keywords:
imbalanced-data strategyimproved PseAACsecretory proteins of Mycobacterium tuberculosissupport vector machine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Immunology

Background:

  • Secretory proteins of Mycobacterium tuberculosis are key virulence factors.
  • Traditional methods for identifying these proteins are laborious and costly.

Purpose of the Study:

  • To develop an advanced bioinformatics model, SecProMTB, for efficient identification of Mycobacterium tuberculosis secretory proteins.
  • To provide a rapid and accurate alternative to conventional experimental approaches.

Main Methods:

  • Utilized an improved pseudo-amino acid composition (PseAAC) algorithm for feature extraction.
  • Implemented a novel imbalanced-data strategy for dataset splitting.
  • Employed feature-ranking algorithms with increment feature selection to mitigate overfitting.
  • Trained and optimized a support vector machine (SVM) model.

Main Results:

  • The SecProMTB model achieved an area under the curve (AUC) of 0.862.
  • Attained an average accuracy of 86% on an independent test set.
  • Demonstrated the efficacy of the proposed bioinformatics approach.

Conclusions:

  • SecProMTB provides a highly accurate and efficient method for identifying Mycobacterium tuberculosis secretory proteins.
  • The developed bioinformatics tool can accelerate research into tuberculosis pathogenesis and vaccine development.
  • The SecProMTB model and associated data are publicly available for broader scientific use.