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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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HypercubeME: two hundred million combinatorially complete datasets from a single experiment.

Laura Avino Esteban1, Lyubov R Lonishin2, Daniil Bobrovskiy3

  • 1Universitat Pompeu Fabra (UPF), Barcelona, Spain.

Bioinformatics (Oxford, England)
|November 20, 2019
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Summary
This summary is machine-generated.

This study introduces an algorithm to efficiently identify higher-order epistasis in large datasets from random mutagenesis experiments. It enables the discovery of complex genetic interactions within biological systems.

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Area of Science:

  • Evolutionary biology
  • Genetics
  • Computational biology

Background:

  • Epistasis, the context-dependent effect of genetic mutations on fitness, is prevalent in evolution.
  • Detecting epistasis typically requires measuring fitness across multiple genotypes, including single and double mutants.
  • Generating comprehensive datasets for higher-order epistasis (combinatorially complete datasets) has been a significant challenge, often relying on manual curation.

Purpose of the Study:

  • To develop an effective algorithm for identifying higher-order epistasis within high-throughput experimental data.
  • To automate the discovery of combinatorially complete genotype datasets from random mutagenesis experiments.

Main Methods:

  • A recursive algorithm was developed to search for hypercube structures within genotype-phenotype data.
  • The algorithm was applied to a real-world dataset from a HIS3 protein study.

Main Results:

  • The algorithm successfully identified all 199,847,053 unique combinatorially complete genotype combinations.
  • These combinations spanned dimensionalities from two to twelve, demonstrating the algorithm's scalability.
  • The findings highlight the potential for discovering higher-order epistasis in existing high-throughput data.

Conclusions:

  • The developed algorithm provides an effective method for analyzing complex genetic interactions in large datasets.
  • This tool can significantly aid researchers in uncovering higher-order epistasis from high-throughput mutagenesis experiments.
  • The availability of the algorithm facilitates further exploration of genotype-phenotype relationships and evolutionary dynamics.