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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.7K
Prediction Intervals01:03

Prediction Intervals

2.9K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.9K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

968
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
968
Survival Tree01:19

Survival Tree

292
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...
292
Regression Toward the Mean01:52

Regression Toward the Mean

6.7K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.7K
Cancer Survival Analysis01:21

Cancer Survival Analysis

556
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
556

You might also read

Related Articles

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

Sort by
Same author

Using functional magnetic resonance imaging to evaluate an acute allograft rejection model in rats.

Magnetic resonance imaging·2019
Same author

QsvR integrates into quorum sensing circuit to control Vibrio parahaemolyticus virulence.

Environmental microbiology·2019
Same author

mi R -15a/15b Cluster Modulates Survival of Mesenchymal Stem Cells to Improve Its Therapeutic Efficacy of Myocardial Infarction.

Journal of the American Heart Association·2019
Same author

Retrospective analysis of Clostridium difficile infection in patients with ulcerative colitis in a tertiary hospital in China.

BMC gastroenterology·2019
Same author

CCCH-type zinc finger antiviral protein is specifically overexpressed in spleen in response to subgroup J avian leukosis virus infection in chicken.

Research in veterinary science·2018
Same author

Mapping Twisted Light into and out of a Photonic Chip.

Physical review letters·2018

Related Experiment Video

Updated: Dec 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Prognostic outcome prediction by semi-supervised least squares classification.

Mingguang Shi1, Zhou Sheng1, Hao Tang1

  • 1School of Electric Engineering and Automation, Hefei University of Technology, Hefei, Anhui, 230009 China.

Briefings in Bioinformatics
|October 23, 2020
PubMed
Summary

This study introduces Rescaled linear square Regression based Least Squares Learning (RRLSL), a novel semi-supervised method for cancer patient prognostic outcome prediction. RRLSL enhances accuracy by integrating multiple molecular data types for robust feature selection and classification.

Keywords:
least squares learningprognostic outcome predictionrescaled linear square regressionsemi-supervised learning

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

393

Related Experiment Videos

Last Updated: Dec 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

393

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Accurate prognostic outcome prediction in cancer is crucial but challenged by small sample sizes.
  • Existing methods struggle with integrating diverse molecular data for robust classification.

Purpose of the Study:

  • To develop a novel semi-supervised feature selection and classification method, Rescaled linear square Regression based Least Squares Learning (RRLSL).
  • To improve prognostic outcome prediction accuracy in cancer patients using multi-omics data.

Main Methods:

  • RRLSL employs least squares regression for feature scaling and ranking across multiple molecular data types.
  • It integrates labeled and unlabeled data to construct a similarity graph.
  • Kernel functions bridge label and geometric information from mRNA and microRNA expression data.
  • A semi-supervised classifier is developed using least squares learning with L2 regularization.

Main Results:

  • RRLSL demonstrated improved performance in prognostic outcome prediction.
  • The method successfully discriminated between recurrent and non-recurrent cancer patients.
  • RRLSL outperformed baseline semi-supervised methods in accuracy and Area Under the Precision Recall Curve (AUPRC).

Conclusions:

  • RRLSL offers a robust approach for prognostic outcome prediction in cancer, particularly with limited sample sizes.
  • The integration of multi-omics data and semi-supervised learning enhances classification accuracy.
  • RRLSL is available as a software package for broader application.