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

Search research articles

Related Experiment Video

Updated: Apr 10, 2026

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

Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer

Nora K Speicher¹, Nico Pfeifer²

¹Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken.

Bioinformatics (Oxford, England)

|June 15, 2015

Summary

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...

You might also read

Related Articles

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

Sort by

Same author

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026

Same author

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.

Nature communications·2025

Same author

Privacy-preserving AUC computation in distributed machine learning with PHT-meDIC.

PLOS digital health·2025

Same author

Privacy-preserving federated unsupervised domain adaptation with application to age prediction from DNA methylation data.

Bioinformatics (Oxford, England)·2025

Same author

Long-term impact of the SARS-CoV-2 pandemic on respiratory viruses in Germany.

BMC public health·2025

Same author

Correction: ENABLE-App-Based Digital Capture and Intervention of Patient-Reported Quality of Life, Adverse Events, and Treatment Satisfaction in Breast Cancer: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2025

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

This summary is machine-generated.

We developed a new computational method to integrate multidimensional cancer data, identifying patient subgroups with distinct survival outcomes and potential treatment responses. This approach improves upon existing methods for personalized cancer therapy.

Area of Science:

Computational biology
Bioinformatics
Cancer research

Background:

Cancer treatment decisions are challenging due to limited therapy options and reliance on single data types for patient stratification.
Integrating multidimensional patient data (e.g., molecular features) can reveal intrinsic tumor characteristics.
Existing computational methods struggle to reliably integrate diverse molecular data for cancer subtype discovery.

Purpose of the Study:

To extend multiple kernel learning for dimensionality reduction to effectively integrate multidimensional cancer data.
To develop a computational framework for identifying biologically meaningful cancer subtypes.
To improve personalized treatment strategies by uncovering novel patient subgroup characteristics.

Main Methods:

Related Experiment Videos

Last Updated: Apr 10, 2026

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

Applied and extended multiple kernel learning for dimensionality reduction.

Incorporated a regularization term to prevent overfitting during optimization.

Utilized multiple kernels per data type to reduce user dependency on selecting optimal kernel functions and parameters.

Main Results:

Identified biologically meaningful subgroups across five cancer types.
Demonstrated significant survival differences between identified subtypes (P values comparable or superior to state-of-the-art methods).
Subtypes reflect combined patterns from diverse data sources and show differential responses to therapies.

Conclusions:

The developed method reliably integrates multidimensional patient data for cancer subtype discovery.
Identified subtypes offer potential for improved cancer treatment decision-making.
The approach enhances the utility of large-scale cancer genomics projects for clinical applications.