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

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Functionally Guided Graph Learning for Robust Cross-Patient Cell-Type Annotation in Single-Cell RNA Sequencing.

Journal of chemical information and modeling·2026

Same author

Flexible artificial lateral line based on luminous flux for underwater velocity vector estimation.

National science review·2026

Same author

Inferring circRNA-Disease Associations via Sparse Topological Representation Learning and Dual-View Decoding.

International journal of molecular sciences·2026

Same author

The Anterolateral Transpedicular Transverse Process Screw Technique: Surgical Techniques, Biomechanical study and Preliminary Outcomes.

Orthopaedic surgery·2026

Same author

Quantitative Analysis of External Urethral Sphincter Stimulation Parameters for Modulating Urinary Output.

bioRxiv : the preprint server for biology·2026

Same author

Kinetically Inert MRI/PET Probes with Myeloperoxidase-Triggered Covalent Capture for Quantitative Imaging of Acute Pancreatitis.

Journal of medicinal chemistry·2026

Search research articles

Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction.

Lei Wang¹, Leon Wong¹, Zhengwei Li¹

¹Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China.

Briefings in Bioinformatics

|September 7, 2022

Summary

This study introduces MLCDA, a novel machine learning framework for predicting circular RNA (circRNA)-disease associations by integrating diverse data. MLCDA accurately identifies potential links, aiding disease diagnosis and treatment strategies.

Keywords:

circRNA circRNA sequences circRNA-disease association deep learning disease ontology

More Related Videos

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

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

Related Experiment Videos

Last Updated: Aug 29, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

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

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

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Circular RNAs (circRNAs) play a role in complex disease regulation.
Identifying circRNA-disease associations is crucial for diagnostics and therapeutics.
Existing prediction methods often rely on limited, single-source data.

Purpose of the Study:

To develop an advanced machine learning framework, MLCDA, for predicting circRNA-disease associations.
To overcome the limitations of existing methods by integrating heterogeneous information sources.
To enhance the accuracy and reliability of circRNA-disease association predictions.

Main Methods:

Proposed a machine learning framework named MLCDA.
Fused multiple sources of heterogeneous information, including circRNA sequences and disease ontology.
Evaluated the framework using a gold standard dataset and real-world case studies.

Main Results:

MLCDA effectively captures complex relationships between circRNAs and diseases.
The framework demonstrates high accuracy in predicting potential circRNA-disease associations.
Case studies show MLCDA significantly outperforms existing prediction methods.

Conclusions:

MLCDA is a valuable tool for predicting circRNA-disease associations.
The framework provides mechanistic insights for disease research.
MLCDA facilitates advancements in disease diagnosis, treatment, and overall therapeutic progress.