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

Improving Translational Accuracy02:07

Improving Translational Accuracy

3.8K
3.8K
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.4K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.4K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
310
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

652
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
652
Multiple Regression01:25

Multiple Regression

4.3K
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...
4.3K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

392
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
392

You might also read

Related Articles

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

Sort by
Same author

Profiling Associations Between IGHG-FCGR Ligand-Receptor Interactions and Disease Progression From Stage 1 and 2 to Stage 3 Type 1 Diabetes.

Diabetes·2025
Same author

Correction: Profiling associations of interactive ligand-receptors (HLA class I and KIR gene products) with the progression to type 1 diabetes among seroconverted participants.

Diabetologia·2025
Same author

Profiling associations of interactive ligand-receptors (HLA class I and KIR gene products) with the progression to type 1 diabetes among seroconverted participants.

Diabetologia·2025
Same author

Two DRB3 residues predictively associate with the progression to type 1 diabetes among DR3 carriers.

JCI insight·2025
Same author

Progression to type 1 diabetes in the DPT-1 and TN07 clinical trials is critically associated with specific residues in HLA-DQA1-B1 heterodimers.

Diabetologia·2024
Same author

Oral Insulin Delay of Stage 3 Type 1 Diabetes Revisited in HLA DR4-DQ8 Participants in the TrialNet Oral Insulin Prevention Trial (TN07).

Diabetes care·2024
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Object-oriented regression for building predictive models with high dimensional omics data from translational

Lue Ping Zhao1, Hamid Bolouri2

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States; Department of Biostatistics and Epidemiology, University of Washington School of Public Health, Seattle, WA, United States.

Journal of Biomedical Informatics
|March 15, 2016
PubMed
Summary
This summary is machine-generated.

Object-Oriented Regression (OOR) offers a novel approach for analyzing high-dimensional omics data (HDOD) in cancer research. This method identifies patient subgroups with distinct prognostic survival risks, aiding in personalized treatment strategies for non-small cell lung cancer.

Keywords:
Big dataClustering analysisGene expressionGeneralized linear modelHigh dimensional dataLASSOLung cancerNearest neighbor approachPenalized regression

More Related Videos

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

2.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K

Related Experiment Videos

Last Updated: Mar 24, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
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

2.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K

Area of Science:

  • Computational Biology and Bioinformatics
  • Oncology and Cancer Research
  • Translational Medicine

Background:

  • High-dimensional omics data (HDOD) generation is routine in translational clinical studies, particularly in oncology.
  • The Cancer Genome Atlas (TCGA) provides valuable omics and clinical data for mining and predictive modeling.
  • Accurate prognostic outcome prediction is crucial for effective cancer patient management.

Purpose of the Study:

  • To introduce and apply an Object-Oriented Regression (OOR) methodology for building prognostic outcome predictive models using HDOD.
  • To leverage OOR for identifying patient exemplars based on HDOD patterns and assessing their prognostic associations.
  • To stratify stage I non-small cell lung cancer (NSCLC) patients by prognostic survival risks beyond traditional classifications.

Main Methods:

  • Developed and applied an Object-Oriented Regression (OOR) methodology to HDOD.
  • Utilized TCGA gene expression data from non-small cell lung cancer patients.
  • Divided data into training and validation sets to build and test the predictive model.

Main Results:

  • Built a predictive model for prognostic survivorship in stage I NSCLC patients using OOR.
  • Successfully stratified patients based on prognostic survival risks derived from HDOD patterns.
  • Validated the association of OOR-derived risk scores with prognostic outcome in the validation dataset (P=0.015).

Conclusions:

  • OOR effectively reduces the penalty of high dimensionality in omics data while maintaining interpretability for clinical practitioners.
  • The OOR-based model provides a valuable tool for identifying high-risk stage I NSCLC patients.
  • Identification of high-risk patients facilitates the development of tailored treatment protocols and post-treatment management plans.