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

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

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.

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

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...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

You might also read

Related Articles

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

Sort by

Same author

Ambient temperature variations and antimicrobial resistance: a multicountry analysis of non-linear relationships and temperature thresholds.

BMJ global health·2026

Same author

Performance of Cardiovascular Polygenic Risk Scores in Carotid Stenosis Identification.

medRxiv : the preprint server for health sciences·2026

Same author

A Gut-Protective TRPV1-Neuroepithelial Axis: Electroacupuncture Reverses Intestinal Damage in Diet-Induced Obesity.

Diabetes, obesity & metabolism·2026

Same author

Proteomic signatures as biomarkers of atherosclerosis burden.

Cardiovascular research·2026

Same author

Association Between First-Trimester Gestational Weight Gain and the Risks of Gestational Diabetes Mellitus and Macrosomia.

Diabetes, metabolic syndrome and obesity : targets and therapy·2026

Same author

Integrating visual and language cues via state space models for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society·2026

Search research articles

Related Experiment Video

Updated: Feb 15, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

A refined maximum predictability for next location prediction with fusion knowledge.

Liuhong Huang^1,2, Zhaocheng He^1,2, Xiying Li^1,2

¹School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China.

|February 13, 2026

Summary

This summary is machine-generated.

This study refines maximum predictability for location prediction by incorporating diverse spatiotemporal knowledge. The new method enhances travel regularity analysis and improves prediction model evaluation.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Related Experiment Videos

Last Updated: Feb 15, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

* Computational Science
* Data Science
* Human Mobility Analysis

Background:

* Existing predictability measures for location prediction often use incomplete spatiotemporal information.
* Quantifying predictability across diverse spatiotemporal data is challenging with current entropic measures.
* Applications of predictability lack detailed analysis of individual travel regularity.

Purpose of the Study:

* To address limitations in current predictability measures for next location prediction.
* To propose a refined method for quantifying maximum predictability using comprehensive spatiotemporal information.
* To enhance the analysis of individual travel regularity and the evaluation of prediction models.

Main Methods:

* Summarized and categorized spatiotemporal information into four types of spatiotemporal knowledge.
* Developed a refined maximum predictability measure integrating fusion knowledge and Shannon entropy.
* Utilized individual spatiotemporal knowledge preferences for travel regularity analysis and model evaluation.

Main Results:

* The proposed refined maximum predictability achieved superior performance on simulation and real-world datasets.
* Achieved a mean absolute error (MAE) of 0.06 on the simulation dataset.
* Demonstrated that personalized spatiotemporal knowledge selection is crucial for effective location prediction.

Conclusions:

* The refined maximum predictability offers a more robust approach to understanding and quantifying location prediction accuracy.
* Personalized utilization of spatiotemporal knowledge significantly improves the performance of location prediction models.
* This research provides valuable insights for designing and enhancing next location prediction systems.