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Related Concept Videos

Cluster Sampling Method01:20

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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...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Updated: Nov 6, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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Swarm: A federated cloud framework for large-scale variant analysis.

Amir Bahmani1,2,3, Kyle Ferriter2,3, Vandhana Krishnan2,3

  • 1Stanford Healthcare Innovation Lab, Stanford University, California, United States of America.

Plos Computational Biology
|May 12, 2021
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Summary
This summary is machine-generated.

Swarm enables federated genomic data analysis across multiple clouds, minimizing data movement. This framework reduces costs, speeds up queries, and enhances security for large-scale genomic variant analysis.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cloud Computing

Background:

  • Analyzing large genomic datasets across disparate cloud platforms presents significant challenges.
  • Current methods often involve substantial data movement, increasing costs and security risks.

Purpose of the Study:

  • To introduce Swarm, a federated computation framework designed for multi-cloud genomic data analysis.
  • To demonstrate Swarm's ability to facilitate cross-platform data interaction with minimal data motion.

Main Methods:

  • Developed Swarm, a federated computation framework.
  • Integrated Swarm with cloud-based data warehousing and query engines: Google Cloud Platform (GCP) BigQuery, Amazon Web Services (AWS) Athena, Apache Presto, and MySQL.
  • Executed common genomic variant inquiries across these platforms using Swarm.

Main Results:

  • Swarm significantly reduced computational costs compared to single-cloud approaches.
  • The framework led to substantial reductions in run-time delays for cross-platform genomic queries.
  • Swarm enhanced security and privacy by minimizing data transfer between cloud environments.

Conclusions:

  • Swarm offers an efficient and secure solution for federated genomic data analysis across multiple cloud platforms.
  • The framework effectively addresses the challenges of large-scale genomic data integration and analysis in multi-cloud settings.