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

Improving Translational Accuracy02:07

Improving Translational Accuracy

12.0K
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...
12.0K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

727
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...
727
Prediction Intervals01:03

Prediction Intervals

2.5K
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. 
2.5K
Neural Regulation01:37

Neural Regulation

40.6K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
40.6K
Position-effect Variegation02:32

Position-effect Variegation

6.7K
In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
6.7K
Epistasis Analysis01:09

Epistasis Analysis

5.4K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.4K

You might also read

Related Articles

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

Sort by
Same author

2-Oxoindoline-based hydroxamic acids: novel HDAC inhibitors with promising anticancer activity.

RSC advances·2026
Same author

Association between myosteatosis and bone mineral density in postmenopausal women.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2026
Same author

Association of the Primary Aldosteronism Severity Classification with Lateralization and Treatment Outcomes.

The Journal of clinical endocrinology and metabolism·2026
Same author

AOP Network Box: An Integrative Framework for Bridging Adverse Outcome Pathways and Biological Networks.

Chemical research in toxicology·2026
Same author

Efficacy and Safety of <i>Salvia miltiorrhiza</i> Extract (SAGX) Compared with Saw Palmetto in Men with Lower Urinary Tract Symptoms: A 12-Week, Randomized, Double-Blind, Parallel-Group Pilot Study.

Nutrients·2026
Same author

Development of Simple Assessment tool for predict imminent risk of Fracture in Elderly women.

The Korean journal of internal medicine·2026

Related Experiment Video

Updated: Oct 18, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

671

An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks.

Ha Young Kim1, Woosung Jeon1, Dongsup Kim2

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Scientific Reports
|September 28, 2021
PubMed
Summary

MTBAN, a novel variant effect prediction tool, overcomes data circularity issues common in genetic disease research. This unsupervised deep generative model accurately predicts protein variant deleteriousness, offering a user-friendly web server for broader accessibility.

Related Experiment Videos

Last Updated: Oct 18, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

671

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate variant effect prediction is crucial for understanding human genetic diseases.
  • Existing supervised predictors often face challenges with data circularity.
  • There is a need for reliable, unsupervised methods to assess variant deleteriousness.

Purpose of the Study:

  • To develop an accurate and reliable variant effect prediction tool.
  • To address the data circularity problem in existing prediction methods.
  • To provide a user-friendly web server for variant effect prediction.

Main Methods:

  • Developed MTBAN (Mutation effect predictor using the Temporal convolutional network and the Born-Again Networks).
  • Applied Born-Again Networks (BAN), a knowledge distillation technique, to a deep autoregressive generative model (mutationTCN).
  • Trained the model in a fully unsupervised manner using evolutionarily related protein sequences, avoiding data circularity.

Main Results:

  • MTBAN demonstrates outstanding predictive ability for human protein variant deleteriousness.
  • The model outperforms other well-known variant effect predictors on a test dataset.
  • Achieved improved performance in variant effect prediction through knowledge distillation.

Conclusions:

  • MTBAN offers a robust, unsupervised approach to variant effect prediction, mitigating data circularity issues.
  • The developed tool provides a significant advancement in predicting the deleteriousness of genetic variants.
  • A freely accessible web server (http://mtban.kaist.ac.kr) is available for MTBAN predictions.