Related Experiment Video

Updated: Aug 19, 2026

Detecting the Lyme Disease Spirochete, Borrelia Burgdorferi, in Ticks Using Nested PCR
07:20

Detecting the Lyme Disease Spirochete, Borrelia Burgdorferi, in Ticks Using Nested PCR

Published on: February 4, 2018

A Step-by-Step Guide to Selecting an Optimal Cut-Off Value Based on the Receiver Operating Characteristic and Youden

Kunal Garg1, Sara Campolonghi2

  • 1Tezted Ltd, Jyväskylä, Finland. kunal.garg@tezted.com.

Methods in Molecular Biology (Clifton, N.J.)
|January 2, 2024
PubMed

Keywords:
Microsoft Excel ROCOptimal cutoffReceiver Operating CharacteristicSensitivitySpecificityYouden Index

More Related Videos

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma07:52

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test05:25

Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test

Published on: July 14, 2023

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...