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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Related Experiment Video

Updated: Mar 2, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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Learning a Structural and Functional Representation for Gene Expressions: To Systematically Dissect Complex Cancer

Yanbo Wang, Quan Liu, Shan Huang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |May 11, 2017
    PubMed
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    This study introduces a novel representation learning method to uncover key gene interaction modules driving complex cancer phenotypes. The approach identifies influential biological building blocks for understanding cancer heterogeneity.

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    Area of Science:

    • Computational biology
    • Genomics
    • Cancer research

    Background:

    • Cancer's heterogeneity poses challenges in understanding complex phenotypes.
    • Identifying underlying biological determinants requires effective computational strategies.
    • Gene interactions are hypothesized to be crucial for cancer phenotype manifestation.

    Purpose of the Study:

    • To develop a representation learning strategy for dissecting cancer phenotypes.
    • To conceptualize gene expression data using influential gene interaction modules.
    • To identify biological building blocks of cancer through computational abstraction.

    Main Methods:

    • A representation learning strategy combined with regularizations was employed.
    • Gene expressions were modeled as a product of meta-genes and their expression levels.
    • A node-based graphical model was used to incorporate gene interaction topological contexts and conditional dependencies.

    Main Results:

    • The developed representation identifies influential modules implicated in neoplastic transformations.
    • The strategy robustly recognizes various cancer phenotypes, outperforming classical methods.
    • Discovered modules are consistent with biological literature, showing shared or specific roles across human cancers.

    Conclusions:

    • The proposed representation learning strategy effectively dissects complex cancer phenotypes.
    • Gene interaction-constrained meta-genes provide valuable insights into cancer biology.
    • This computational approach aids in understanding cancer heterogeneity and identifying therapeutic targets.