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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
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The dissolution of intimate relationships presents complex emotional and psychological challenges, particularly when emotional bonds are strong, the relationship is long-standing, and perceived alternatives are limited. This distress often intensifies in romantic breakups, where the initiator may experience greater turmoil than the rejected partner. Contributing factors include residual attachment, guilt over causing pain, and uncertainty about how to manage the situation. The stress is further...
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Phase Transitions02:31

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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach.

Jianxiao Liu, Zonglin Tian, Yingjie Xiao

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 4, 2018
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    Summary
    This summary is machine-generated.

    This study introduces an improved Bayesian network method for constructing gene regulatory networks (GRNs). The enhanced algorithm accurately identifies gene relationships, outperforming existing methods on benchmark and maize RNA-seq data.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Constructing gene regulatory networks (GRNs) is crucial for understanding cellular complexity but remains challenging.
    • Existing methods often struggle with high-order conditional independence tests in large datasets.

    Purpose of the Study:

    • To develop an improved Bayesian network learning method for robust GRN construction.
    • To enhance the accuracy of identifying gene regulatory relationships.

    Main Methods:

    • An improved three-phase dependency analysis algorithm (TPDA) using Bayesian network learning.
    • Utilizing Gaussian kernel probability density estimation for entropy estimation and calculating mutual information.
    • Applying the method to benchmark datasets and a large maize RNA-seq dataset.

    Main Results:

    • The improved TPDA method significantly outperforms nine other Bayesian network learning algorithms on benchmark datasets with large sample sizes.
    • The enhanced algorithm demonstrates superior performance compared to the original TPDA and other methods on a real maize gene expression dataset.

    Conclusions:

    • The improved TPDA Bayesian network learning method offers a more reliable approach for constructing gene regulatory networks.
    • This method is effective for analyzing complex biological data, including large-scale gene expression datasets like RNA-seq.