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

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

You might also read

Related Articles

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

Sort by

Same author

Representation and generalizability in clinical research: Back to basics.

Journal of clinical and translational science·2026

Same author

Physical Implementation of Reinforcement Learning via a Signal Summation Process in a Dual-Input Synaptic Transistor: Photoinduced Dipole Inversion of Au(I) Complex with Charge Traps of cPVP.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Analog Signal Summation for Reinforcement Learning via Simultaneous Light-Voltage Modulation in a Synaptic Device.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same author

Role of Amorphous Phases in Mixed Conduction of Conjugated Regioblock Copolymers for Organic Electrochemical Synaptic Transistors.

Advanced materials (Deerfield Beach, Fla.)·2025

Same author

Hardware Implementation of On-Chip Hebbian Learning Through Integrated Neuromorphic Architecture.

Advanced materials (Deerfield Beach, Fla.)·2025

Same author

Analysis of the Relationship Between <i>NF-κB1</i> and Cytokine Gene Expression in Hematological Malignancy: Leveraging Explained Artificial Intelligence and Machine Learning for Small Dataset Insights.

International journal of medical sciences·2025

Same journal

Peptidomics in the Spotlight: Advanced Sample Treatment Techniques and Analytical Insights.

Advances in experimental medicine and biology·2026

Same journal

Methods for the Investigation of Protein-Ligands Interactions.

Advances in experimental medicine and biology·2026

Same journal

Sample Preparation Strategies for Microbial Cell Surface Proteomics: Integrating Shaving and Shotgun Approaches.

Advances in experimental medicine and biology·2026

Same journal

Proteomic Sample Preparation for the Petroleum Industry: A Biocorrosion Case Study.

Advances in experimental medicine and biology·2026

Same journal

Proteomic and Functional Comparison of Extracellular Vesicles from Wild-Type and Lyn-Deficient Stromal Cells.

Advances in experimental medicine and biology·2026

Same journal

Proteomic Analysis of Histone Sequence Variants and Post-translationally Modified Forms.

Advances in experimental medicine and biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 10, 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

Statistical Approaches to Candidate Biomarker Panel Selection.

Heidi M Spratt¹, Hyunsu Ju²

¹The University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555-1148, USA. hespratt@utmb.edu.

Advances in Experimental Medicine and Biology

|December 16, 2016

Summary

This summary is machine-generated.

Statistical analysis for biomarker discovery involves data visualization, preprocessing, hypothesis testing, and feature reduction. Machine learning then refines candidate biomarkers for classification. This systematic approach ensures robust biomarker identification.

Keywords:

Candidate biomarker selection Data clustering Data consistency Data inspection Data normalization Data transformations Machine learning Outlier detection

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Mar 10, 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

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Biostatistics
Biomarker Discovery
Computational Biology

Background:

Biomarker candidate statistical analysis is integral to the biomarker development pipeline.
Key steps include data visualization, preprocessing, and hypothesis testing.

Purpose of the Study:

To outline the sequential statistical analysis process for identifying robust biomarker candidates.
To detail methods for handling data complexity and reducing candidate lists for effective classification.

Main Methods:

Data visualization for outlier detection and initial group comparisons.
Data preprocessing to handle outliers, missing values, and assess normality.
Hypothesis testing to identify differentially expressed proteins.
Feature reduction techniques to narrow down candidate biomarkers.
Application of unsupervised or supervised learning for classification.

Main Results:

The process identifies differentially expressed proteins from complex datasets.
Feature reduction effectively narrows down a large set of proteins to a manageable number of candidates.
Machine learning models are applied to the reduced feature set for downstream classification purposes.

Conclusions:

A structured statistical approach is essential for robust biomarker candidate identification.
The described pipeline integrates various analytical methods from initial data exploration to final classification.
This systematic analysis facilitates the development of reliable biomarker panels.