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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Event-based text mining for biology and functional genomics.

Sophia Ananiadou, Paul Thompson, Raheel Nawaz

    Briefings in Functional Genomics
    |June 8, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Researchers are developing text mining tools to automatically extract biochemical reaction events from scientific literature. These systems improve the efficiency of functional genomics research by enabling structured searches for specific biological interactions.

    Keywords:
    event extractionsemantic annotationsemantic searchtext mining

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Assessing genome function necessitates mapping biological entities to biochemical reactions.
    • The biomedical literature is a vast resource for this information, but its rapid growth poses challenges for efficient data retrieval.
    • Text mining research focuses on extracting structured 'events' (relationships between biochemical entities) to address this challenge.

    Purpose of the Study:

    • To provide an overview of recent advancements in automatic event extraction from biomedical literature.
    • To highlight the role of event extraction in functional genomics and semantic search applications.
    • To discuss the resources, systems, and community efforts driving progress in this field.

    Main Methods:

    • Review of annotated corpora used for training event extraction systems.
    • Analysis of state-of-the-art event extraction systems and their performance.
    • Examination of community-driven shared tasks that promote system improvement.
    • Identification of applications and emerging research directions in event extraction.

    Main Results:

    • Significant progress has been made in developing automatic event extraction systems.
    • Community shared tasks have demonstrably improved system quality, coverage, and scalability.
    • These systems enable sophisticated semantic search for specific biochemical reactions.

    Conclusions:

    • Automatic event extraction is crucial for advancing functional genomics and enabling efficient literature-based discovery.
    • Continued research and community collaboration are vital for enhancing the capabilities and applications of these text mining tools.