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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.2K
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

6.3K
Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
6.3K
RNA-seq03:21

RNA-seq

9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K

You might also read

Related Articles

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

Sort by
Same author

piR-LGBM: A Sparse Autoencoder-Enhanced Gradient Boosting Framework to Uncover Disease-Associated piRNAs.

Omics : a journal of integrative biology·2026
Same author

A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.

Molecular diversity·2024
Same author

Inferring Potential CircRNA-Disease Associations via Deep Autoencoder-Based Classification.

Molecular diagnosis & therapy·2020
Same author

Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.

Molecular genetics and genomics : MGG·2020

Related Experiment Video

Updated: Jun 14, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.7K

An ensemble approach for circular RNA-disease association prediction using variational autoencoder and genetic

C M Salooja1, Arjun Sanker1, K Deepthi2

  • 1Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala-682022, India.

Journal of Bioinformatics and Computational Biology
|August 31, 2024
PubMed
Summary

This study introduces VAGA-CDA, a novel model predicting circular RNA (circRNA) and disease associations. The model effectively identifies potential links, aiding in disease diagnosis and treatment strategies.

Keywords:
Circular RNAassociationsdiseasegenetic algorithmrandom forestvariational autoencoder

More Related Videos

Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

12.2K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

Related Experiment Videos

Last Updated: Jun 14, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.7K
Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

12.2K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Circular RNAs (circRNAs) are non-coding RNAs with regulatory functions in the human genome.
  • CircRNAs are implicated in various diseases, including cancer, Alzheimer's, and diabetes.
  • Identifying circRNA-disease associations is crucial for diagnostics and therapeutics.

Purpose of the Study:

  • To develop a computational model for predicting novel circRNA-disease associations.
  • To leverage machine learning for enhanced feature extraction and selection in circRNA data.
  • To improve the accuracy of circRNA-disease association prediction.

Main Methods:

  • Utilized a variational autoencoder (VAE) for feature extraction from augmented circRNA-disease data.
  • Employed a genetic algorithm (GA) for optimized feature selection and dimensionality reduction.
  • Applied a Random Forest classifier for predicting novel circRNA-disease associations.
  • Augmented data using the synthetic minority oversampling technique (SMOTE).

Main Results:

  • The VAGA-CDA model achieved high predictive performance with AUC values of 0.9644 (5-fold CV) and 0.9628 (10-fold CV).
  • The VAE effectively extracted relevant features, while the GA optimized feature selection.
  • Case studies demonstrated the model's robustness and reliability in predicting circRNA-disease links.

Conclusions:

  • The VAGA-CDA model presents a robust and accurate approach for predicting circRNA-disease associations.
  • The integration of VAE, GA, and Random Forest offers a powerful framework for bioinformatics-driven disease research.
  • This predictive model holds significant potential for advancing disease diagnosis, prevention, and treatment strategies.