Bayesian Analysis
Bayesian Analysis - Related Videos
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Cited by 6 •
2012
Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
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Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
13:44
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Cited by 13 •
2013
We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
FAQs
A prior probability represents the starting plausibility assigned to a hypothesis before the current evidence is assessed. When patient or study data are limited, that starting value can have a stronger effect on the resulting posterior probability. Making the prior explicit allows investigators to examine how existing knowledge influences conclusions rather than leaving that influence implicit.
The likelihood describes how compatible the observed clinical data are with a hypothesis or proposed effect. Evidence that is more consistent with one hypothesis than another shifts the posterior probability toward the better-supported explanation. This step connects the statistical model to the actual patient or study observations and determines how strongly new data modify the initial probability.
A posterior probability summarizes the updated state of uncertainty after prior knowledge and observed evidence have been combined. Because it incorporates the information already considered, it can become the prior for a subsequent analysis when additional evidence arrives. This creates a continuing framework for accumulating knowledge across clinical studies, patients, or sequential decisions.
First, investigators identify the clinical hypothesis and specify an initial prior probability. They then describe how the observed patient or study data relate to that hypothesis through the likelihood. Bayes' theorem combines these elements to produce a posterior probability, which is interpreted for the clinical question and may provide the starting point for later analyses.
In diagnostic testing, the framework combines an initial probability for a clinical hypothesis with evidence from a patient's test or other observed data. The resulting posterior probability expresses how the new evidence changes the assessment of that hypothesis. This patient-specific interpretation can support clinical decision-making while keeping the remaining uncertainty visible.
Clinical researchers can combine existing knowledge about a treatment with evidence collected from patients or a trial. The updated probability helps characterize what the data imply about the treatment effect while preserving uncertainty in the conclusion. Bayesian analysis therefore provides a way to interpret trial findings in the context of prior information rather than considering new results in isolation.