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Related Concept Videos

Quality Control01:05

Quality Control

1.4K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality of Water01:19

Quality of Water

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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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.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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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.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Pulse amplitude and quality01:17

Pulse amplitude and quality

3.0K
Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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Updated: Jan 23, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Ontology-based metabolomics data integration with quality control.

Patricia Buendia1, Ray M Bradley1, Thomas J Taylor1

  • 1INFOTECH Soft, Inc., 1201 Brickell Ave. Suite 220, Miami, FL 33131, USA.

Bioanalysis
|June 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces computational methods for robust metabolomics meta-analysis, ensuring data quality and accurate interpretation. These methods identify reliable biomarkers by integrating diverse datasets, validating findings across studies.

Keywords:
data integrationdiabetes use casemeta-analysismetabolomicsontology-based expert systemquality control

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Area of Science:

  • Computational biology
  • Metabolomics
  • Bioinformatics

Background:

  • Meta-analysis of metabolomics data presents challenges in data quality and interpretation.
  • Integrating diverse datasets requires robust quality control (QC) methods.

Purpose of the Study:

  • To address complications in metabolomics meta-analysis through advanced computational methods.
  • To ensure data quality, metadata completeness, and accurate cross-study interpretation.

Main Methods:

  • Development of an integrated system of QC methods for metabolomics results.
  • Evaluation of data acquisition strategies and metabolite identification processes.
  • Utilizing an ontology knowledge base and rule-based system for data integration and QC verification.

Main Results:

  • A diabetes meta-analysis study successfully identified putative biomarkers differentiating cohorts.
  • The QC methods demonstrated effectiveness in assessing metabolomics data integration.

Conclusions:

  • The presented methods ensure the validity and reliability of meta-analysis for integrated metabolomics data.
  • This approach enhances the accuracy of biomarker discovery across multiple studies.