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

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification.

IEEE transactions on pattern analysis and machine intelligence·2023

Same author

Self-Supervised Learning for Label- Efficient Sleep Stage Classification: A Comprehensive Evaluation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023

Same author

Self-Supervised Autoregressive Domain Adaptation for Time Series Data.

IEEE transactions on neural networks and learning systems·2022

Same author

Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond.

IEEE transactions on cybernetics·2021

Same author

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2021

Same author

An improved random forest-based computational model for predicting novel miRNA-disease associations.

BMC bioinformatics·2019

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Ensemble positive unlabeled learning for disease gene identification.

Peng Yang¹, Xiaoli Li¹, Hon-Nian Chua¹

¹Data Analytics Department, Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.

|May 13, 2014

Summary

This study introduces EPU, an ensemble-based positive unlabeled learning (PU learning) framework. EPU effectively identifies novel disease genes by integrating multiple data sources and machine learning classifiers, improving prediction accuracy and robustness.

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 Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Related Experiment Videos

Last Updated: Apr 30, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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 Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Numerous genes are confirmed as causative for human diseases.
Machine learning can identify novel disease-associated genes from existing data.
Positive unlabeled learning (PU learning) is effective for this task, using confirmed disease genes (P) and candidate genes (U).

Purpose of the Study:

To develop an effective PU learning framework for disease gene identification.
To integrate multiple biological data sources and machine learning classifiers.
To enhance prediction accuracy and robustness by minimizing bias from single data sources or methods.

Main Methods:

Proposed an ensemble-based PU learning framework (EPU).
Integrated data from multiple biological sources for training PU learning classifiers.
Employed an ensemble of PU learning classifiers to combine predictions.

Main Results:

EPU demonstrated significantly improved performance compared to state-of-the-art methods.
Evaluations across six disease groups confirmed EPU's accuracy and robustness.
The integrated approach minimized bias from individual data sources and algorithms.

Conclusions:

EPU provides an effective framework for disease gene identification.
Integrating diverse data and ensemble methods enhances prediction accuracy.
The framework can incorporate future biological and computational resources for improved predictions.