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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
Prediction Intervals01:03

Prediction Intervals

3.4K
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. 
3.4K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.4K
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...
1.4K
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
Associative Learning01:27

Associative Learning

1.4K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Machine learning-assisted SERS for rapid and accurate early screening of cervical cancer.

Analytical and bioanalytical chemistry·2026
Same author

Cerebellar Repetitive Transcranial Magnetic Stimulation (rTMS) With Modified Proprioceptive Neuromuscular Facilitation (PNF) Balloon Dilation for Dysphagia After Brainstem and Cerebellar Infarction: A Case Report.

Cureus·2026
Same author

TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.

Chemical science·2026
Same author

P4NSU: Projection-Based Pretraining for Nonlinear Sparse Unmixing in Spectral Imaging.

Analytical chemistry·2026
Same author

Factors associated with willingness to receive influenza vaccination among adults in Shanghai, China: a study based on the health belief model.

BMC public health·2026
Same author

Mass Spectrometry Imaging of Glycosphingolipid C═C Position Isomers by On-tissue Capture-Epoxidation on a Diselenide-Functionalized Covalent Organic Framework.

Analytical chemistry·2026

Related Experiment Video

Updated: Feb 9, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
08:40

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

18.0K

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Ming Wen1, Peisheng Cong2, Zhimin Zhang1

  • 1College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China.

Bioinformatics (Oxford, England)
|June 6, 2018
PubMed
Summary

DeepMirTar accurately predicts microRNA (miRNA) targets on messenger RNAs (mRNAs) using a deep-learning approach. This method enhances the identification of miRNA-mRNA interactions, aiding gene expression regulation studies.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K

Related Experiment Videos

Last Updated: Feb 9, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
08:40

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

18.0K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are crucial regulators of gene expression, operating via RNA silencing and post-transcriptional modification of messenger RNAs (mRNAs).
  • Identifying specific miRNA binding sites on mRNAs is challenging due to complex interaction mechanisms, hindering research progress.
  • In silico prediction methods offer a valuable strategy to accelerate the identification of potential miRNA-target interactions, reducing experimental costs.

Purpose of the Study:

  • To develop and implement DeepMirTar, a novel deep-learning framework for high-accuracy, site-level prediction of human miRNA targets.
  • To leverage diverse features, including expert-designed and raw data, for robust representation of miRNA-target interactions.
  • To evaluate DeepMirTar's performance against existing state-of-the-art methods.

Main Methods:

  • Development of DeepMirTar, a deep-learning model incorporating various feature levels (high-level, low-level, raw data) to represent miRNA-target sites.
  • Training and validation of the model on human miRNA and mRNA data, considering both canonical and non-canonical binding.
  • Comparative analysis of DeepMirTar against established machine learning algorithms and miRNA target prediction tools.

Main Results:

  • DeepMirTar demonstrated superior predictive performance in identifying miRNA-target sites compared to existing methods.
  • The model effectively utilizes a comprehensive set of features to capture the nuances of miRNA binding.
  • The study successfully implemented and validated a deep-learning approach for precise miRNA target prediction.

Conclusions:

  • DeepMirTar offers an accurate and efficient computational tool for predicting miRNA-target interactions at the site level.
  • The findings contribute to a better understanding of miRNA-mediated gene regulation.
  • DeepMirTar is available as an open-source tool to facilitate further research in the field.