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Related Concept Videos

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...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
Law of Segregation01:49

Law of Segregation

When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

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Related Experiment Video

Updated: Jun 28, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Conditional random pattern algorithm for LOH inference and segmentation.

Ling-Yun Wu1, Xiaobo Zhou, Fuhai Li

  • 1Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.

Bioinformatics (Oxford, England)
|November 1, 2008
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately infers loss of heterozygosity (LOH) by considering genotyping errors and SNP distances. This conditional random pattern (CRP) model improves upon traditional hidden Markov models (HMM) for tumor evolution studies.

Related Experiment Videos

Last Updated: Jun 28, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Loss of heterozygosity (LOH) is a key mechanism in tumor evolution.
  • Detecting LOH typically uses tumor and normal sample genotypes.
  • Genotyping errors and non-informative SNPs complicate LOH detection.

Purpose of the Study:

  • To develop a novel algorithm for LOH inference and segmentation.
  • To improve LOH detection accuracy in the presence of genotyping errors and non-informative SNPs.

Main Methods:

  • Developed a conditional random pattern (CRP) model for LOH inference.
  • Incorporated SNP distance, genotyping error rate, and heterozygous rate into the model.
  • Tested the CRP model on simulated and real Affymetrix Human Mapping 500K SNP array data.

Main Results:

  • The CRP model explicitly considers factors influencing LOH detection.
  • Experimental results demonstrate superior performance compared to conventional hidden Markov models (HMM).
  • The algorithm effectively infers LOH status even with non-informative SNPs.

Conclusions:

  • The novel CRP model offers a more accurate approach to LOH inference and segmentation.
  • This method enhances the analysis of tumor evolution by improving LOH detection.
  • Software for the CRP algorithm is available upon request.