Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

You might also read

Related Articles

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

Sort by

Same author

Orchestrator multi-agent clinical decision support system for secondary headache diagnosis in primary care.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same author

A multimodal generative model for structured and unstructured electronic health records.

npj health systems·2026

Same author

Predicting the timing of first sustained cognitive worsening in Alzheimer's disease using real-world clinical data and machine learning.

medRxiv : the preprint server for health sciences·2026

Same author

i2b2-ML: module to facilitate machine learning in the informatics for integrating biology and the bedside platform.

JAMIA open·2026

Same author

Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals.

JAMA network open·2026

Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Improving classification performance with discretization on biomedical datasets.

Jonathan L Lustgarten¹, Vanathi Gopalakrishnan, Himanshu Grover

¹Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|November 13, 2008

Summary

This summary is machine-generated.

Discretization enhances machine learning classification performance by selecting variables and transforming data. This method significantly improves algorithms like Support Vector Machines and Random Forests for high-dimensional genomic and proteomic data analysis.

More Related Videos

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Jun 28, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

Computational biology
Bioinformatics
Machine learning

Background:

High-dimensional genomic and proteomic data present challenges for machine learning.
Algorithms like Support Vector Machines and Random Forests are robust to high dimensionality.
Other algorithms, such as Naïve Bayes, are sensitive to data dimensionality.

Purpose of the Study:

To investigate the impact of data discretization on machine learning classification performance.
To evaluate discretization as a variable selection technique.
To assess improvements in classification for various machine learning algorithms.

Main Methods:

Applying discretization techniques to transform continuous variables into discrete ones.
Utilizing machine learning algorithms including Support Vector Machines, Random Forests, and Naïve Bayes.
Evaluating classification performance on high-dimensional genomic and proteomic datasets.

Main Results:

Discretization significantly improves classification performance for Support Vector Machines and Random Forests.
Discretization also enhances the performance of dimensionality-sensitive algorithms like Naïve Bayes.
Discretization functions as an effective variable selection method.

Conclusions:

Data discretization is a valuable preprocessing step for improving machine learning classification in high-dimensional biological data.
Discretization offers benefits for both robust and sensitive machine learning algorithms.
The dual role of discretization in data transformation and variable selection is key to performance enhancement.