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

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by
Same author

Sasanlimab taken together with Bacillus Calmette-Guérin (BCG) compared with BCG alone in people with high-risk non-muscle invasive bladder cancer: a plain language summary.

Future oncology (London, England)·2026
Same author

Stage- and smoking-associated microRNA expression in lung adenocarcinoma.

Translational lung cancer research·2025
Same author

Author Correction: Sasanlimab plus BCG in BCG-naive, high-risk non-muscle invasive bladder cancer: the randomized phase 3 CREST trial.

Nature medicine·2025
Same author

Sasanlimab plus BCG in BCG-naive, high-risk non-muscle invasive bladder cancer: the randomized phase 3 CREST trial.

Nature medicine·2025
Same author

Physical Activity and Dexamethasone for Cancer-Related Fatigue: A Preliminary Placebo-Controlled, Randomized, Double-Blind Trial.

Journal of the National Comprehensive Cancer Network : JNCCN·2025
Same author

Left Ventricular Ejection Fraction in Patients With Ovarian Cancer Treated With Avelumab, Pegylated Liposomal Doxorubicin, or Both.

The oncologist·2023
Same journal

A systematic scoping review of cancer-related anemia treatment: Comparative trial outcomes, current guidelines, and future perspectives.

Seminars in oncology·2026
Same journal

Steroid-induced tumor lysis syndrome in solid tumors: A case report and review of the literature.

Seminars in oncology·2026
Same journal

PSMA PET/CT staging in intermediate-risk prostate cancer: Toward risk-adapted implementation.

Seminars in oncology·2026
Same journal

Angiogenesis and the corresponding antiangiogenic therapy in gastroenteropancreatic neuroendocrine neoplasms.

Seminars in oncology·2026
Same journal

Post-translational regulation of steroidogenesis and its clinical relevance in hormone responsive cancers.

Seminars in oncology·2026
Same journal

Physics-informed machine learning for tumor microenvironment-responsive nanomedicine: Recent updates.

Seminars in oncology·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Learning curves in classification with microarray data.

Kenneth R Hess1, Caimiao Wei

  • 1Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, 77030, USA. khess@odin.mdacc.tmc.edu

Seminars in Oncology
|February 23, 2010
PubMed
Summary
This summary is machine-generated.

Learning curves in machine learning show performance improvement with more data. This study quantifies these learning curves in cancer genomics using inverse power law models, revealing classifier performance differences.

More Related Videos

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Related Experiment Videos

Last Updated: Jun 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning

Background:

  • Task performance often improves with experience, a phenomenon described as a learning curve.
  • In supervised machine learning, algorithm performance typically increases with more training data.
  • This relationship is often visualized as a negatively accelerating learning curve.

Purpose of the Study:

  • To quantify learning curves in supervised machine learning using inverse power law models.
  • To assess the performance and efficiency of different classification algorithms on large clinical cancer genomic datasets.
  • To evaluate how the number of training samples impacts classifier performance in genomic studies.

Main Methods:

  • Progressively increasing the number of training observations to train classification algorithms.
  • Plotting algorithm performance against the number of training observations to generate learning curves.
  • Fitting inverse power law models to the generated learning curves.
  • Applying three classifiers (diagonal linear discriminant analysis, K-nearest-neighbor, support vector machines) to four cancer genomic datasets with varying numbers of top genes (5, 50, 500, 5,000).

Main Results:

  • Inverse power law models provided a reasonable fit to the progressively sampled data.
  • Significant diversity in learning curves was observed across different classifiers applied to the same datasets.
  • Some classifiers demonstrated rapid performance increases with more data, while others showed minimal improvement.
  • The study highlights the importance of classifier efficiency, especially given the high cost of genomic samples.

Conclusions:

  • Learning curves, quantified by inverse power law models, are valuable for assessing classifier efficiency in machine learning.
  • Classifier performance and efficiency vary considerably, even on the same genomic datasets.
  • Utilizing learning curves with a modest number of training samples (over 50) can effectively evaluate predictive efficiency in genomic studies.