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

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

You might also read

Related Articles

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

Sort by

Same author

AugGCL: Multimodal graph learning for spatial transcriptomics analysis with enhanced gene and morphological data.

PLoS computational biology·2026

Same author

Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.

Briefings in bioinformatics·2025

Same author

Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.

Briefings in bioinformatics·2025

Same author

Research on Predicting the Turnover of Graduates Using an Enhanced Random Forest Model.

Behavioral sciences (Basel, Switzerland)·2024

Same author

MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset.

Bioinformatics (Oxford, England)·2023

Same author

Deep structure integrative representation of multi-omics data for cancer subtyping.

Bioinformatics (Oxford, England)·2022

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 21, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Deep Subspace Mutual Learning for cancer subtypes prediction.

Bo Yang¹, Ting-Ting Xin¹, Shan-Min Pang²

¹School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.

Bioinformatics (Oxford, England)

|September 3, 2021

Summary

This summary is machine-generated.

This study introduces Deep Subspace Mutual Learning (DSML), a computational framework for precise cancer subtype prediction. DSML integrates multi-omics data, improving understanding and diagnosis of complex diseases.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Oct 21, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Accurate cancer subtype prediction is crucial for effective diagnosis and treatment.
Cancer etiology is complex, involving multiple omics levels, necessitating integrative analysis.

Purpose of the Study:

To develop a novel computational framework for integrative multi-omics analysis.
To enhance cancer subtype prediction by leveraging deep learning for subspace structure learning.

Main Methods:

Proposed Deep Subspace Mutual Learning (DSML) framework utilizing deep neural networks.
Simultaneously learned subspace structures within individual and combined omics data.
Clustering on multi-level, single-level, and partial-level omics data for subtype prediction.

Main Results:

DSML demonstrated comparable or superior performance against state-of-the-art integrative methods.
Experiments were conducted across five cancer types using three omics data levels from The Cancer Genome Atlas.
The framework effectively integrates diverse omics data for improved subtype identification.

Conclusions:

DSML offers a powerful approach for integrative multi-omics analysis in cancer research.
The framework facilitates more accurate cancer subtype prediction, aiding clinical applications.
Publicly available implementation promotes further research and development in cancer bioinformatics.