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Related Concept Videos

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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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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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Maximizing the Utility of Cancer Transcriptomic Data.

Yu Xiang1, Youqiong Ye1, Zhao Zhang2

  • 1Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; These authors contributed equally.

Trends in Cancer
|November 25, 2018
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This review explores computational tools for analyzing cancer transcriptome data. These resources help identify novel biomarkers and therapeutic targets by examining complex RNA events and genetic variations.

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cancer transcriptomeexogenous RNAnoncoding RNApost-transcriptional regulationtranscribed genetic variant

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Large-scale transcriptomic profiling of cancer samples is crucial for understanding cancer complexity.
  • Consortia like The Cancer Genome Atlas have generated extensive cancer transcriptomic datasets.
  • Advances in data analysis are key to discovering new cancer biomarkers and therapeutic targets.

Purpose of the Study:

  • To review computational resources for in-depth analysis of cancer transcriptomic data.
  • To highlight methods for identifying, quantifying, and assessing the functional and clinical impact of transcriptomic events.
  • To discuss the application of these approaches to other complex diseases.

Main Methods:

  • Review of existing computational tools and methodologies for transcriptomic data mining.
  • Focus on identifying noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants.
  • Evaluation of approaches for determining the functional effects and clinical utility of identified transcriptomic events.

Main Results:

  • Identification of diverse computational resources for deep mining of cancer transcriptomic data.
  • Categorization of resources based on their ability to analyze various transcriptomic events.
  • Demonstration of the utility of these computational approaches for biomarker and therapeutic target discovery.

Conclusions:

  • Computational resources are essential for unlocking the potential of cancer transcriptomic data.
  • Advanced analysis enables a deeper understanding of cancer complexity and facilitates the discovery of novel targets.
  • These methods have broad applicability to other complex diseases, maximizing research impact.