Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

RNA-seq

RNA-seq

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...

Improving Translational Accuracy

Improving Translational Accuracy

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...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

You might also read

Related Articles

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

Sort by

Same author

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026

Same author

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026

Same author

Intelligent mobile health management for the risk of gastrointestinal bleeding in anticoagulated patients with cardiovascular disease.

Revista da Escola de Enfermagem da U S P·2026

Same author

MVCL: A Contrastive Learning Model with Multi-view Networks for Driver Gene Prediction.

IEEE journal of biomedical and health informatics·2026

Same author

GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.

Briefings in bioinformatics·2026

Same author

MambaSSM: efficient segmentation of brain structures in anisotropic 3D EM images via state-space models.

Frontiers in neuroscience·2026

Search research articles

Related Experiment Video

Updated: Jul 17, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease

Lin Yuan^1,2,3, Jiawang Zhao^1,2,3, Zhen Shen⁴

¹Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.

Plos Computational Biology

|August 31, 2023

Summary

This study introduces iCircDA-NEAE, a novel deep learning model for predicting circular RNA (circRNA)-disease associations. The model effectively utilizes diverse data types to improve prediction accuracy and identify potential disease biomarkers.

More Related Videos

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jul 17, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

Biomedical informatics
Computational biology
Genomics

Background:

Circular RNAs (circRNAs) are increasingly recognized for their roles in human diseases.
Predicting circRNA-disease associations aids in understanding disease mechanisms, diagnosis, and biomarker discovery.
Existing deep learning methods often underutilize biometric data and extract suboptimal features.

Purpose of the Study:

To develop a novel deep learning model, iCircDA-NEAE, for accurate circRNA-disease association prediction.
To address limitations of current methods by integrating diverse data sources and enhancing feature extraction.
To improve the identification of potential circRNA-disease relationships for clinical applications.

Main Methods:

Developed the iCircDA-NEAE deep learning model.
Integrated disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity.
Employed accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE) for feature extraction.

Main Results:

iCircDA-NEAE demonstrated significantly superior performance compared to existing methods on the circR2Disease dataset.
16 out of the top 20 predicted circRNA-disease pairs were validated through existing literature.
The model effectively predicted novel potential circRNA-disease associations.

Conclusions:

iCircDA-NEAE offers a powerful new tool for circRNA-disease association prediction.
The model's ability to integrate multiple data types and extract robust features enhances its predictive capabilities.
This approach holds promise for advancing disease pathogenesis research and biomarker discovery.