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

Residuals and Least-Squares Property

Residuals and Least-Squares Property

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...

You might also read

Related Articles

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

Sort by

Same author

Aerith: Visualization and Annotation of Isotopic Enrichment Patterns of Peptides and Metabolites with Stable Isotope Labeling from Proteomics and Metabolomics.

Analytical chemistry·2025

Same author

Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.

Nature communications·2025

Same author

MetaLP: An integrative linear programming method for protein inference in metaproteomics.

PLoS computational biology·2022

Same author

Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil.

Nature ecology & evolution·2018

Same author

Protein expression and significance of VEGF, EGFR and MMP-9 in non-small cell lung carcinomas.

Asian Pacific journal of cancer prevention : APJCP·2011

Same author

Bilirubin attenuates bufadienolide-induced ventricular arrhythmias and cardiac dysfunction in guinea-pigs by reducing elevated intracellular Na(+) levels.

Cardiovascular toxicology·2011

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 16, 2025

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

Predicting psoriasis using routine laboratory tests with random forest.

Jing Zhou¹, Yuzhen Li¹, Xuan Guo²

¹Department of Dermatology, Second Affiliated Hospital of Harbin Medical University, Harbin, PR China.

|October 19, 2021

Summary

This summary is machine-generated.

This study developed a machine learning model to predict psoriasis using routine lab tests, achieving 86.9% accuracy. The findings highlight the potential of accessible data for early disease detection.

More Related Videos

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

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

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: Oct 16, 2025

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

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

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

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:

Dermatology
Biomedical Informatics
Machine Learning

Background:

Psoriasis is a chronic inflammatory skin disease affecting 125 million globally.
It significantly impacts physical and emotional quality of life.
Predicting psoriasis using routine laboratory tests is valuable for clinical practice.

Purpose of the Study:

To develop a predictive model for psoriasis using only routine hospital laboratory tests.
To identify key biomarkers from routine tests for psoriasis prediction.

Main Methods:

Collected data from 466 psoriasis patients and 520 healthy controls.
Utilized Boruta feature selection to identify relevant variables from 81 laboratory tests.
Constructed a Random Forest classification model and validated it using 10-fold cross-validation (30 repetitions).

Main Results:

Achieved an average classification accuracy of 86.9%.
Selected 26 notable features, with 15 confirmed by prior research.
Identified novel potential biomarkers for psoriasis prediction.

Conclusions:

Machine learning models show significant potential for psoriasis prediction using routine hospital tests.
Accessible laboratory data can be leveraged for effective disease modeling.
Further investigation into newly identified features is warranted.