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Related Concept Videos

Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Published on: September 5, 2025

Mining the Gene Wiki for functional genomic knowledge.

Benjamin M Good1, Douglas G Howe, Simon M Lin

  • 1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.

BMC Genomics
|December 15, 2011
PubMed
Summary
This summary is machine-generated.

The Gene Wiki provides a low-cost method for generating gene annotations using Wikipedia. This approach can create valuable ontology-based gene annotations to improve biological data analysis and disease research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Ontology-based gene annotations are crucial for organizing and analyzing genome-scale data.
  • Collecting these annotations is resource-intensive.
  • The Gene Wiki leverages Wikipedia for low-cost, collaborative gene annotation assembly.

Purpose of the Study:

  • To define and assess a computational strategy for converting Gene Wiki text into ontology-based gene annotations.
  • To explore the generation of structured annotations using the Gene Ontology (GO) and the Human Disease Ontology (DO).

Main Methods:

  • Utilized text from over 10,000 Gene Wiki articles.
  • Developed a computational strategy to extract and structure gene annotations.
  • Employed the Gene Ontology and Human Disease Ontology for annotation generation.

Main Results:

  • Generated 2,983 candidate annotations using DO and 11,022 using GO.
  • Estimated precision of 90-93% for DO annotations and 48-64% for GO annotations.
  • Demonstrated systematic improvement in gene set enrichment analyses using the generated annotations.

Conclusions:

  • The Gene Wiki is a valuable resource for generating ontology-based gene annotations.
  • These annotations can enhance curated gene annotation databases.
  • The findings facilitate knowledge-based statistical analyses and improve biological data interpretation.