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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Types of Selection01:46

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Combinatorial Gene Control02:33

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Gene Evolution - Fast or Slow?02:05

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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.
In contrast, regions which code...
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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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A robust and stable gene selection algorithm based on graph theory and machine learning.

Subrata Saha1, Ahmed Soliman2, Sanguthevar Rajasekaran3

  • 1Irving Medical Center, Columbia University, New York, NY, 10032, USA.

Human Genomics
|November 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a robust gene selection algorithm for analyzing gene expression data with phenotypes. The method enhances prediction accuracy and stability, outperforming existing approaches in identifying key genes for medical conditions.

Keywords:
Gain ratio (GR)Linear Support Vector Machine (LSVM)Robust and Stable Gene Selection Algorithm (RSGSA)Support vector machine-recursive feature elimination (SVM-RFE)Symmetric Uncertainty (SU)

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression data with phenotypes is crucial for identifying disease-related genes and potential drug targets.
  • High-dimensional 'omics' data (e.g., microarray, RNA-seq, GWAS) presents challenges due to small sample sizes and a large number of features, leading to instability in gene selection.
  • Instability in gene selection occurs when different subsets of features yield similar optimal results, complicating accurate gene identification.

Purpose of the Study:

  • To develop a robust and stable supervised gene selection algorithm for gene expression datasets with phenotypes.
  • To identify a reliable set of genes with enhanced prediction capabilities from complex biological data.
  • To address the instability issue inherent in high-dimensional 'omics' data analysis.

Main Methods:

  • Developed a supervised gene selection algorithm incorporating class and instance level perturbations to ensure robustness and stability.
  • Evaluated the algorithm's performance using 10 real-world gene expression microarray datasets with associated phenotypes.
  • Conducted biological enrichment analyses using Gene Ontology-Biological Processes (GO-BP), Disease Ontology (DO) terms, and biological pathways.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to state-of-the-art methods in terms of both stability and classification accuracy.
  • Experimental evaluations on 10 diverse gene expression datasets validated the algorithm's effectiveness.
  • Biological enrichment analysis provided further insights into the biological relevance of the selected genes.

Conclusions:

  • The developed supervised gene selection method is effective and efficient for analyzing gene expression data with phenotypes.
  • The algorithm offers a robust solution for identifying stable and predictive gene sets, overcoming common challenges in high-dimensional data.
  • Results indicate significant improvements in stability and predictive accuracy, supporting its utility in biomedical research.