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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

414
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
414
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
342

You might also read

Related Articles

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

Sort by
Same author

Inhibition of matrine against gastric cancer cell line MNK45 growth and its anti-tumor mechanism.

Molecular biology reports·2011
Same author

Evolution of activation patterns during long-duration ventricular fibrillation in pigs.

American journal of physiology. Heart and circulatory physiology·2011
Same author

A new feruloyl amide derivative from the fruits of Tribulus terrestris.

Natural product research·2011
Same author

High-amylose rice improves indices of animal health in normal and diabetic rats.

Plant biotechnology journal·2011
Same author

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

Statistical methods in medical research·2011
Same author

All-optical virtual private network and ONUs communication in optical OFDM-based PON system.

Optics express·2011

Related Experiment Video

Updated: Apr 17, 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

2.4K

Integrative Analysis of "-Omics" Data Using Penalty Functions.

Qing Zhao1, Xingjie Shi2, Jian Huang3

  • 1Department of Biostatistics, School of Public Health, Yale University.

Wiley Interdisciplinary Reviews. Computational Statistics
|February 19, 2015
PubMed
Summary

Integrative omics analysis combines multiple datasets for robust findings. This review highlights penalized methods, including sparse meta-analysis and raw data pooling, for enhanced insights.

Keywords:
Integrative analysismarker selectionomics datapenalization

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K

Related Experiment Videos

Last Updated: Apr 17, 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

2.4K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K

Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Integrative omics analysis enhances insights by pooling information across multiple datasets.
  • Penalization methods offer a powerful framework for integrative analysis.
  • Existing reviews often lack a focused examination of penalized approaches.

Purpose of the Study:

  • To provide a comprehensive review of penalization methods for integrative omics analysis.
  • To discuss sparse meta-analysis and raw data pooling approaches.
  • To examine advanced penalization techniques and computational aspects.

Main Methods:

  • Focus on penalization methods for omics data integration.
  • Review sparse meta-analysis (summary statistics pooling).
  • Review integrative analysis with raw data pooling.
  • Discuss contrasted and Laplacian penalization for complex data structures.

Main Results:

  • Penalization methods offer effective strategies for omics data integration.
  • Sparse meta-analysis and raw data pooling are key approaches.
  • Advanced penalization techniques accommodate finer data structures.
  • Computational aspects like algorithms and parameter selection are crucial.

Conclusions:

  • Penalization methods are vital for effective integrative omics analysis.
  • Further research into limitations and extensions of these methods is warranted.
  • This review provides a foundation for applying and developing advanced integrative techniques.