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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

32
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...
32
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

158
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
158
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

107
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
107
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.2K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

331
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...
331

You might also read

Related Articles

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

Sort by
Same author

Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.

Scientific reports·2026
Same author

Fibroblast- and Macrophage-Derived Thrombospondin-1 Orchestrates the Fibroinflammatory Niche in Metabolic Dysfunction-Associated Steatohepatitis-Induced Fibrosis.

Diabetes & metabolism journal·2026
Same author

Label-Free Quantification of Bilirubin Using a Refractive Index-Insensitive Nanolaminate SERS Substrate.

Biosensors·2026
Same author

Corrigendum to "Molecular role of developmentally regulated GTP-binding protein 1 in coordinating osteoclast and osteoblast differentiation during bone remodeling" [Mol. Cells 49 (2026) 100342].

Molecules and cells·2026
Same author

Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.

PloS one·2026
Same author

Analysis of severity in COVID-19 patients by using longitudinal immune profiles.

iScience·2026
Same journal

Baicalein alleviates high glucose-induced mesangial cell fibrosis and inflammation in diabetic nephropathy: roles of AMPK activation and TGF-β1 inhibition.

Genes & genomics·2026
Same journal

BMP/Ventx1.1 axis modulates the multiciliogenesis in the early Xenopus epidermis.

Genes & genomics·2026
Same journal

DKK1 activated the NF-κB pathway by binding with CKAP4 to induce PASMC oxidative stress and promote pulmonary arterial hypertension.

Genes & genomics·2026
Same journal

Correction to: Cochlear synaptic vulnerability scales with pathological noise intensity in a noise-induced hearing loss model.

Genes & genomics·2026
Same journal

Chloroplast genome characterization of Adenophora taquetii and comparative analysis with related Adenophora species.

Genes & genomics·2026
Same journal

MiR-125a C > T, MiR-152 C > T, MiR-938 G > A, and MiR-491 G > A single nucleotide polymorphisms and their influence on genetic susceptibility to type 2 diabetes.

Genes & genomics·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.2K

Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.

Suhyun Hwangbo1,2, Sungyoung Lee2, Md Mozaffar Hosain3

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Korea.

Genes & Genomics
|September 26, 2024
PubMed
Summary
This summary is machine-generated.

HisCoM-KernelS identifies survival pathways using RNA-sequencing data, accounting for complex gene effects and pathway correlations. This method improves upon traditional approaches for pancreatic cancer survival analysis.

Keywords:
HisCoM-KernelSKEGG pathwaysKernel machine regressionPermutation testSurvival phenotypes

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.1K
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

981

Related Experiment Videos

Last Updated: Jun 12, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.2K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.1K
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

981

Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Computational Biology

Background:

  • High-throughput sequencing, including RNA-sequencing (RNA-seq), has revolutionized gene expression analysis.
  • Traditional pathway analysis methods often overlook inter-pathway correlations and overlapping biomarkers.
  • Existing approaches typically assume linear gene effects on phenotypes, limiting their scope.

Purpose of the Study:

  • To develop the HisCoM-KernelS model for identifying survival phenotype-related pathways.
  • To accommodate complex, nonlinear relationships between genes and survival outcomes.
  • To account for inter-pathway correlations in pathway-based survival analysis.

Main Methods:

  • Applied the HisCoM-KernelS model to the TCGA pancreatic ductal adenocarcinoma (PDAC) RNA-seq dataset.
  • Utilized kernel machine regression to model pathway effects on survival, incorporating gene-pathway structures.
  • Estimated model parameters via alternating least squares and assessed pathway significance using permutation tests.

Main Results:

  • HisCoM-KernelS identified significant pathways associated with pancreatic cancer survival.
  • The model demonstrated a superior balance of detection rate and significant pathways compared to HisCoM-PAGE, Global Test, GSEA, and CoxKM.
  • Gaussian kernel integration in HisCoM-KernelS enhanced performance.

Conclusions:

  • HisCoM-KernelS effectively extends pathway analysis to survival outcomes by capturing nonlinear gene effects and inter-pathway correlations.
  • The model's application to TCGA PDAC data highlights its utility in identifying biologically relevant pathways.
  • HisCoM-KernelS provides a robust tool for survival phenotype research using high-throughput sequencing data.