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 Experiment Videos

Bayesian state space models for inferring and predicting temporal gene expression profiles.

Yulan Liang1, Arpad Kelemen

  • 1Department of Biostatistics, University at Buffalo, The State University of New York, 252A2 Farber Hall, 3435 Main Street, Buffalo, NY 14214, USA. yliang@buffalo.edu

Biometrical Journal. Biometrische Zeitschrift
|July 20, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies <i>UCN</i>-Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies.

Cancers·2026
Same author

Telehealth interventions for alcohol use disorder: A systematic review.

Liver research (Beijing, China)·2025
Same author

Event-triggered affine formation of second-order multi-agent systems via complex-valued Laplacian.

ISA transactions·2024
Same author

Unraveling the Degradation and Air-Induced Healing Mechanisms of Halide Perovskites under Proton Irradiation.

ACS applied materials & interfaces·2024
Same author

Use of Smartphone Apps for Improving Physical Function Capacity in Cardiac Patient Rehabilitation: Systematic Review.

JMIRx med·2023
Same author

A Physical Activity Mobile Game for Hematopoietic Stem Cell Transplant Patients: App Design, Development, and Evaluation.

JMIRx med·2023
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

This study introduces novel Bayesian state space models to predict gene expression dynamics, effectively handling unevenly spaced time course data. The models accurately capture genomic behavior for disease-associated gene expression profiling.

Area of Science:

  • Genomics
  • Computational Biology
  • Biostatistics

Background:

  • Predicting gene dynamic behavior is crucial in genomic research.
  • Existing methods often assume evenly distributed time intervals and stationary models, which is unsuitable for real-world microarray data.
  • Unevenly spaced, short time courses with abrupt changes pose significant challenges for accurate genetic dynamics prediction.

Purpose of the Study:

  • To develop advanced Bayesian state space models for inferring and predicting gene expression profiles.
  • To address the limitations of existing techniques in handling non-stationary and unevenly sampled time course data.
  • To improve the understanding of gene expression dynamics in disease-associated genomic research.

Main Methods:

  • Developed univariate time-varying Bayesian state space models with time-variant transition and observation matrices.

Related Experiment Videos

  • Developed multivariate Bayesian state space models incorporating temporal correlation structures in covariance matrix estimations.
  • Treated unevenly spaced time courses and unseen time points as hidden state variables, employing Bayesian approaches with MCMC algorithms for parameter and variable estimation.

    • Main Results:

    • The proposed models successfully infer and predict gene expression profiles from complex genomic data.
    • Demonstrated the models' capability to handle non-stationarity and temporal correlations inherent in microarray data.
    • Validated the models' effectiveness on multiple tissue polygenetic Affymetrix datasets, showing accurate capture of genomic dynamic behavior.

    Conclusions:

    • The novel Bayesian state space models offer a robust solution for predicting gene dynamic behavior, even with challenging data.
    • These models provide a significant advancement for genomic research, particularly in disease-associated gene expression analysis.
    • The proposed methodology effectively addresses the limitations of traditional approaches for time course gene expression data.