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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: Jun 3, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Published on: May 17, 2019

Data Synthesis and Tool Development for Exploring Imaging Genomic Patterns.

Sungeun Kim, Li Shen, Andrew J Saykin

    IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
    |September 28, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an imaging genomic browsing system to analyze brain imaging and genetic data, overcoming computational challenges for discovering genetic influences on brain structure and function.

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    Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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    Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

    Published on: May 9, 2011

    Area of Science:

    • Neuroscience
    • Genetics
    • Bioinformatics

    Background:

    • Advanced brain imaging and high-throughput genotyping offer new avenues for studying genetic influences on brain structure and function.
    • Analyzing high-dimensional neuroimaging and genomic data presents significant computational challenges, hindering comprehensive joint analysis.

    Purpose of the Study:

    • To develop an integrated imaging genomic browsing system for exploring the relationships between genetic variations and brain phenotypes.
    • To address computational bottlenecks in analyzing large-scale neuroimaging and genomic datasets.

    Main Methods:

    • Development of a realistic synthetic dataset with known relationships between imaging phenotypes and genotypes.
    • Implementation of a browsing system for visualizing genome-wide statistical associations between brain imaging phenotypes and genotypes.
    • Focus on exploring the neuroanatomical distribution of these statistical results.

    Main Results:

    • Demonstration of the system's functionality using the synthesized data.
    • The system facilitates the exploration of statistical associations across the genome.
    • Initial progress in creating a tool for integrated neuroimaging and genomic data analysis.

    Conclusions:

    • The developed imaging genomic browsing system shows potential for discovering significant imaging genomic associations.
    • Visual evaluation within the system aids in identifying key genetic links to brain characteristics.
    • The system architecture allows for future extensions and broader applications in genetic research.