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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: May 23, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Querying genomic databases: refining the connectivity map.

Mark R Segal1, Hao Xiong, Henrik Bengtsson

  • 1University of California-San Francisco, CA, USA.

Statistical Applications in Genetics and Molecular Biology
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

The Connectivity Map (cmap) tool aids drug discovery by linking disease gene expression to drug mechanisms. Refinements in analysis reveal that inference methods are more critical than ordered query handling for cmap

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Related Experiment Videos

Last Updated: May 23, 2026

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Pharmacogenomics

Background:

  • High-throughput gene expression technologies generate large biological databases.
  • Gene expression repositories offer significant scientific value beyond individual studies.
  • The Connectivity Map (cmap) is a tool designed to identify potential drug efficacy for diseases based on gene expression signatures.

Purpose of the Study:

  • To refine the Connectivity Map (cmap) analysis for improved drug discovery and biological knowledge enhancement.
  • To develop novel methods for measuring similarity with ordered gene expression queries.
  • To propose an alternative inference approach using empirical null distributions for cmap.

Main Methods:

  • Developed new metrics for measuring closeness between ordered gene expression signatures and experiments.
  • Implemented an alternative inference method based on empirical null distributions.
  • Utilized a customized database of gene expression experiments from drug treatments.

Main Results:

  • Accommodating ordered queries in cmap analysis is less critical than the inference mode.
  • The refined inference approach captures database dependencies and scope effectively.
  • Re-evaluation of cmap findings highlights the importance of the inference strategy.

Conclusions:

  • The Connectivity Map (cmap) is valuable for connecting poorly understood diseases to known drug mechanisms.
  • Improved inference methods enhance the utility of cmap for biological discovery.
  • Further development in inference strategies can optimize the application of gene expression repositories in drug discovery.