Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

431
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
431
Multiple Regression01:25

Multiple Regression

4.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.1K
Applications of Integration to Find Centers of Mass01:30

Applications of Integration to Find Centers of Mass

91
Rotational equilibrium provides a natural framework for defining the center of mass of a system. For a plank balanced on a pivot with two unequal masses, equilibrium is achieved when the net torque about the pivot is zero. Torque is defined as the product of a force and its perpendicular distance from the pivot. When the torques due to all forces cancel, the pivot coincides with the center of mass of the system.For a system composed of several discrete point masses, the center of mass lies at...
91
Classification of Systems-II01:31

Classification of Systems-II

523
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
523
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.2K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.2K
Test for Homogeneity01:23

Test for Homogeneity

2.4K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.4K

You might also read

Related Articles

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

Sort by
Same author

Specific Circular RNA Signature of Endothelial Cells: Potential Implications in Vascular Pathophysiology.

International journal of molecular sciences·2024
Same author

Nuclear HMGB1 protects from nonalcoholic fatty liver disease through negative regulation of liver X receptor.

Science advances·2022
Same author

Feature selection for kernel methods in systems biology.

NAR genomics and bioinformatics·2022
Same author

Ten simple rules for switching from face-to-face to remote conference: An opportunity to estimate the reduction in GHG emissions.

PLoS computational biology·2021
Same author

Conserved white-rot enzymatic mechanism for wood decay in the Basidiomycota genus Pycnoporus.

DNA research : an international journal for rapid publication of reports on genes and genomes·2020
Same author

Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL.

Scientific reports·2020

Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

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

Published on: September 20, 2024

2.2K

Unsupervised multiple kernel learning for heterogeneous data integration.

Jérôme Mariette1, Nathalie Villa-Vialaneix1

  • 1MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France.

Bioinformatics (Oxford, England)
|October 28, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multiple kernel framework for integrating diverse omics datasets in systems biology. The method enhances exploratory analysis and improves biological system representation, as demonstrated with metagenomic and cancer data.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Related Experiment Videos

Last Updated: Feb 20, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

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

Published on: September 20, 2024

2.2K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing generates diverse omics datasets.
  • Integrating heterogeneous omics data is crucial for systems biology but remains challenging.
  • Existing methods struggle with the diverse data types produced by omics studies.

Purpose of the Study:

  • To develop a flexible framework for integrating multiple, heterogeneous omics datasets.
  • To enable robust exploratory analysis of multi-omics data.
  • To improve the representation and interpretation of biological systems using integrated omics data.

Main Methods:

  • A multiple kernel framework is proposed for integrating diverse datasets.
  • The framework offers solutions for learning consensus or topology-preserving meta-kernels.
  • Kernel Principal Component Analysis (kernel PCA) and kernel Self-Organizing Maps (kernel SOM) are utilized.

Main Results:

  • The framework successfully integrated metagenomic (TARA Oceans) and multi-omics (The Cancer Genome Atlas) datasets.
  • It retrieved previous findings and provided new insights into sample structures in metagenomic data.
  • Integration improved the representation of the biological system in breast cancer data compared to single-omic strategies.

Conclusions:

  • The proposed multiple kernel framework effectively integrates heterogeneous omics data for systems biology.
  • The method enhances exploratory data analysis and biological system representation.
  • The R package mixKernel provides an accessible implementation of the framework.