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Related Concept Videos

Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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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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Discretization of gene expression data revised.

Cristian A Gallo, Rocio L Cecchini, Jessica A Carballido

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    Summary
    This summary is machine-generated.

    Discretization is crucial for gene expression data analysis using machine learning. This review examines state-of-the-art techniques and key considerations for selecting appropriate methods to improve gene interaction inference.

    Keywords:
    data miningdata preprocessingdiscretizationgene expression analysisgene expression datamachine learning

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Gene expression data is vital for understanding gene interactions and functions.
    • Data mining and machine learning algorithms often require data discretization for gene expression analysis.
    • The choice of discretization method significantly impacts the performance of inference algorithms.

    Purpose of the Study:

    • To review current state-of-the-art discretization techniques for gene expression data.
    • To identify key factors influencing the selection of discretization methods.
    • To provide guidance for optimizing gene expression data analysis.

    Main Methods:

    • Comprehensive literature review of discretization techniques.
    • Analysis of challenges and considerations in applying discretization to gene expression data.
    • Synthesis of current knowledge on discretization approaches.

    Main Results:

    • Identified various discretization methods applicable to gene expression data.
    • Highlighted the critical role of discretization in gene interaction inference.
    • Outlined essential criteria for selecting appropriate discretization strategies.

    Conclusions:

    • Effective discretization is paramount for accurate gene expression data analysis.
    • Careful selection of discretization techniques enhances the reliability of machine learning models.
    • This review serves as a guide for researchers in choosing optimal discretization methods.