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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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Related Experiment Video

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

BLOG: Bayesian longitudinal omics with group constraints.

Livia Popa1, Sumanta Basu1, Myung Hee Lee2

  • 1Department of Statistics and Data Science, Cornell University, New York, USA.

Statistical Applications in Genetics and Molecular Biology
|May 19, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Bayesian regression methods for identifying computational biomarkers in longitudinal omics data. These approaches enhance biomarker discovery and control false discoveries, showing high accuracy in simulations and a Tuberculosis study.

Keywords:
Bayes factorsBayesian regressionbiomarker discoverygroup lassolongitudinal omicsvariable selection

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics

Background:

  • Clinical researchers seek computational biomarkers from short-term longitudinal omics data.
  • Accurate biomarker identification is crucial for disease understanding and treatment.

Purpose of the Study:

  • To develop and evaluate Bayesian regression and variable selection methods for longitudinal omics data.
  • To enhance biomarker discovery and control false discovery rates.

Main Methods:

  • Univariate approach using Zellner's g-prior with SURE or sqrt(n) tuning.
  • Multivariate approach using Bayesian group lasso with spike and slab priors.
  • Utilized first difference (Δ) scale for longitudinal predictors and responses.

Main Results:

  • Zellner's g-prior approach demonstrated high specificity and sensitivity in identifying target metabolites.
  • Bayesian group lasso also effectively selected target metabolites in simulations.
  • Methods were compared against linear mixed effect models on simulated and Tuberculosis study data.

Conclusions:

  • The proposed Bayesian methods offer improved inference and prediction for biomarker identification.
  • Automated hyperparameter selection enhances the robustness of the Zellner's g-prior approach.
  • These computational tools advance the discovery of reliable biomarkers from complex omics datasets.