Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

225
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
225
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

986
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
986
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.7K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.7K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

7.0K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
7.0K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

545
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
545

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Metabolic alterations in Snyder-Robinson syndrome lymphoblasts are ameliorated by phenylbutyrate treatment.

Molecular genetics and metabolism·2026
Same author

Standardized rate of hospitalization for violent injuries among different generations in counties and cities in Taiwan from 2000 to 2015.

Medicine·2023
Same author

The Synergetic Impact of Physical Activity and Fruit and Vegetable Consumption on the Risk of Depression in Taiwanese Adults.

International journal of environmental research and public health·2022
Same author

A new test for autism spectrum disorder: Metabolic data from different cell types.

Data in brief·2021
Same author

Chemoreversal Agents from Taiwanofungus Genus and Their More Potent Methyl Derivatives Targeting Signal Transducer and Activator of Transcription 3 (STAT3) Phosphorylation.

Pharmaceuticals (Basel, Switzerland)·2021
Same author

Bioactive naphthoquinones and triterpenoids from the fruiting bodies of Taiwanofungus salmoneus.

Bioorganic chemistry·2021

Related Experiment Video

Updated: Feb 10, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

1.0K

Multiple approaches to data-mining of proteomic data based on statistical and pattern classification methods.

Jacob W Tatay1, Xin Feng, Nancy Sobczak

  • 1University of Wisconsin Parkside, Kenosha, WI, USA.

Proteomics
|September 16, 2003
PubMed
Summary

This study introduces novel data-mining tools for analyzing mass spectrometry protein data to classify subjects and identify disease-related protein expression differences. The methods offer a broad approach for future research in proteomics and disease diagnostics.

More Related Videos

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K
Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

5.4K

Related Experiment Videos

Last Updated: Feb 10, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

1.0K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K
Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

5.4K

Area of Science:

  • Biochemistry
  • Proteomics
  • Data Science

Background:

  • Mass spectrometry generates complex protein expression data.
  • Distinguishing between diseased and non-diseased subjects is crucial for diagnostics.
  • Existing analytical tools may have limitations in handling large-scale proteomics datasets.

Purpose of the Study:

  • To develop and apply novel data-mining tools for analyzing mass spectrometry protein data.
  • To classify subjects into disease and non-disease groups.
  • To identify specific protein expression differences between these groups.

Main Methods:

  • Applied a variety of conventional and non-conventional data-mining methodologies.
  • Utilized mass spectrometry data from 41 subjects.
  • Focused on two core problems: subject classification and differential protein expression analysis.

Main Results:

  • Achieved separation of subjects into two classifications.
  • Identified differences in protein expression data between subject groups.
  • Observed both overlap and discrepancies in results across different methodologies.

Conclusions:

  • The study introduced potentially useful tools for mass spectrometry data analysis.
  • The applied methodologies offer a broad approach for future research.
  • This work paves the way for advanced techniques in proteomics and disease biomarker discovery.