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Related Concept Videos

Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...

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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

Efficient sampling for Bayesian inference of conjunctive Bayesian networks.

Thomas Sakoparnig1, Niko Beerenwinkel

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland.

Bioinformatics (Oxford, England)
|July 12, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to understand the order of mutations in cancer development. The Bayesian approach improves the analysis of large cancer genome datasets, offering better insights into cancer progression.

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Cancer progresses through mutation accumulation and clonal expansion.
  • The precise order of genetic mutations in cancer remains largely unknown.
  • Large-scale cancer genome data necessitates advanced computational analysis methods.

Purpose of the Study:

  • To develop a novel computational method for inferring mutation order in cancer.
  • To analyze large cancer genome datasets more effectively.
  • To improve understanding of cancer progression pathways.

Main Methods:

  • Development of a Bayesian inference scheme for Conjunctive Bayesian Networks.
  • Implementation of an efficient Markov Chain Monte Carlo (MCMC) sampling scheme.
  • Application to simulated and real cancer genome datasets.

Main Results:

  • The proposed Bayesian method outperforms traditional approaches on simulated data.
  • The MCMC sampler effectively overcomes local optima in complex dependency structures.
  • Bayesian analysis provides advantages over maximum-likelihood methods for cancer datasets.

Conclusions:

  • The developed Bayesian inference scheme is a powerful tool for analyzing cancer genome data.
  • This method enhances the elucidation of mutation order and cancer progression.
  • The R package offers a practical resource for researchers in the field.