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Related Concept Videos

Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Percentile01:18

Percentile

A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.Percentiles divide ordered data into hundredths. To score in the...
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Video

Updated: Jun 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

UFFizi: a generic platform for ranking informative features.

Assaf Gottlieb1, Roy Varshavsky, Michal Linial

  • 1Tel Aviv University, Ramat Aviv, Israel. assafgot@tau.ac.il

BMC Bioinformatics
|June 8, 2010
PubMed
Summary
This summary is machine-generated.

Unsupervised Feature Filtering (UFF) identifies key genes from complex datasets using an entropy measure. The UFFizi web-tool provides an efficient method for feature selection and outlier detection in biological data analysis.

Related Experiment Videos

Last Updated: Jun 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Feature selection is crucial for analyzing complex biological data, such as gene expression.
  • Unsupervised methods are less common than supervised ones for feature selection.
  • Unsupervised Feature Filtering (UFF) utilizes an entropy measure from Singular Value Decomposition (SVD) for ranking and selecting features.

Purpose of the Study:

  • To analyze the statistical properties of UFF and develop an efficient entropy calculation.
  • To create a web-tool (UFFizi) for implementing the UFF algorithm.
  • To propose criteria for dataset amenability to UFF and introduce an Unsupervised Detection of Outliers (UDO) method.

Main Methods:

  • Statistical analysis of UFF properties.
  • Development of an efficient entropy approximation for UFF.
  • Implementation of UFF and UDO within the UFFizi web-tool.

Main Results:

  • An efficient approximation for UFF's entropy measure was developed.
  • Novel criteria for assessing dataset suitability for UFF were proposed.
  • The UFFizi web-tool was created, demonstrating UFF and UDO on gene and microRNA expression datasets for viral infection and cancer.

Conclusions:

  • UFF's statistical properties effectively distinguish selected features.
  • The UFFizi framework, based on UFF, is applicable to various diseases.
  • UFFizi is available as a web-tool for biological data analysis.