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

Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.8K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.8K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
Prediction Intervals01:03

Prediction Intervals

3.3K
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. 
3.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
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...
1.2K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.2K
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...
1.2K
Computed Tomography01:10

Computed Tomography

8.1K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
8.1K

You might also read

Related Articles

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

Sort by
Same author

EnhancerMatcher: comparing cell-type-specific enhancer activity of DNA sequences using deep convolutional neural networks and explainable AI.

NAR genomics and bioinformatics·2025
Same author

Redundant and Singular Regulatory Elements Underlie the Rapidly Evolving Pigmentation of Drosophila.

Molecular biology and evolution·2025
Same author

<i>EnhancerDetector</i> : Enhancer Discovery from Human to Fly via Interpretable Deep Learning.

bioRxiv : the preprint server for biology·2025
Same author

SCRMshaw: Supervised cis-regulatory module prediction for insect genomes.

PloS one·2024
Same author

Regulatory genome annotation of 33 insect species.

eLife·2024
Same author

Mechanisms of transcriptional regulation in <i>Anopheles gambiae</i> revealed by allele-specific expression.

Proceedings. Biological sciences·2024
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.9K

Computational enhancer prediction: evaluation and improvements.

Hasiba Asma1, Marc S Halfon2,3,4,5,6,7

  • 1Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA.

BMC Bioinformatics
|April 7, 2019
PubMed
Summary
This summary is machine-generated.

We developed pCRMeval, a computational pipeline to evaluate enhancer prediction tools for genome annotation. This tool assesses prediction accuracy and improves methods like SCRMshaw, enhancing cis-regulatory module discovery.

More Related Videos

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
07:34

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Published on: November 17, 2023

1.2K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K

Related Experiment Videos

Last Updated: Jan 26, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.9K
Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
07:34

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Published on: November 17, 2023

1.2K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate identification of cis-regulatory modules (CRMs) is crucial for genome annotation.
  • Computational methods complement experimental approaches for CRM discovery.
  • Robust evaluation of CRM prediction tool performance (sensitivity and specificity) is essential.

Purpose of the Study:

  • Introduce pCRMeval, a pipeline for in silico evaluation of CRM prediction tools.
  • Assess the precision and relative sensitivity of enhancer prediction methods.
  • Provide a general framework for evaluating and comparing CRM prediction tools.

Main Methods:

  • pCRMeval compares predictions against experimentally-validated CRMs in Drosophila melanogaster.
  • The pipeline estimates precision and relative sensitivity of prediction methods.
  • For supervised methods, pCRMeval assesses training set sensitivity.

Main Results:

  • pCRMeval was used to evaluate and refine the SCRMshaw CRM prediction method, resulting in SCRMshaw_HD.
  • SCRMshaw_HD demonstrated improved prediction numbers while maintaining sensitivity and specificity.
  • SCRMshaw_HD showed robust performance even on less well-assembled genomes.

Conclusions:

  • pCRMeval offers a versatile framework for evaluating CRM prediction methods, especially supervised ones.
  • The pipeline facilitates testing and improvement of individual prediction tools.
  • pCRMeval serves as a valuable platform for comparing different CRM prediction methods.