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

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...

Survival Tree

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...

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.

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

You might also read

Related Articles

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

Sort by

Same author

Shifts in Pediatric Respiratory Virus Trends After the COVID-19 Pandemic: Insights From a 12-Year Hospital-Based Study in South Korea.

International journal of pediatrics·2026

Same author

The connection between neonatal jaundice and neurodevelopment later in life: a national birth cohort study.

BMC public health·2026

Same author

Prevalence of Immediate-Type Food Allergy and Food-Induced Anaphylaxis in Korean Schoolchildren.

Allergy, asthma & immunology research·2026

Same author

Neural representations of popularity and leadership status relate to conformity in daily life.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Cross-assay RNA modeling reveals cancer biomarkers.

bioRxiv : the preprint server for biology·2026

Same author

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Jun 20, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Link prediction accuracy on real-world networks under non-uniform missing-edge patterns.

Xie He¹, Amir Ghasemian², Eun Lee³

¹Department of Mathematics, Dartmouth College, Hanover, NH, United States of America.

|July 18, 2024

Summary

Link prediction accuracy varies significantly based on how network data is collected. This study guides researchers in choosing algorithms suited for non-uniform missing data patterns common in real-world networks.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 20, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Network Science
Data Mining
Machine Learning

Background:

Real-world network datasets often have missing edges due to data collection biases.
Uniform missing data is a common, yet often unrealistic, assumption for evaluating link prediction algorithms.

Purpose of the Study:

To investigate how different non-uniform missing-edge patterns impact link prediction accuracy.
To compare the performance of various link prediction algorithms under diverse missing data scenarios.
To provide guidance for selecting appropriate link prediction methods based on network data characteristics.

Main Methods:

Analysis of 9 link prediction algorithms from 4 families.
Evaluation across 20 distinct missing-edge patterns, categorized into 5 groups.
A comparative simulation study using 250 real-world network datasets from 6 domains.

Main Results:

Significant variations in link prediction algorithm performance were observed across different missing-edge patterns.
The study highlights the substantial impact of non-uniform missing data on evaluation outcomes.
Algorithm performance is highly dependent on the specific characteristics of the missing data.

Conclusions:

The assumption of uniform missing data can lead to misleading evaluations of link prediction methods.
Researchers should consider the data collection process and resulting missing-edge patterns when selecting algorithms.
This work offers a framework for choosing link prediction tools tailored to real-world network data.