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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Related Experiment Video

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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

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Published on: July 18, 2013

A pattern recognition approach to infer time-lagged genetic interactions.

Cheng-Long Chuang1, Chih-Hung Jen, Chung-Ming Chen

  • 1Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.

Bioinformatics (Oxford, England)
|March 14, 2008
PubMed
Summary

The Pattern Recognition (PARE) approach effectively infers time-lagged genetic interactions from time-course microarray data. This machine learning method integrates gene expression data and existing knowledge to predict gene interactions with high accuracy.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Inferring time-lagged genetic interactions from time-course microarray data is challenging due to limited time points and numerous genes.
  • Existing methods struggle to capture complex, non-linear relationships in gene expression patterns over time.
  • Accurate identification of gene interactions is crucial for understanding complex biological pathways and networks.

Purpose of the Study:

  • To develop and validate a novel approach, Pattern Recognition (PARE), for inferring time-lagged genetic interactions.
  • To integrate microarray gene expression data (MGED) with existing biological knowledge using machine learning.
  • To predict novel gene interactions and pathways, particularly those involved in DNA repair.

Main Methods:

  • The PARE approach utilizes a non-linear score to identify gene pairs with distinct time lags.
  • It extracts non-linear characteristics from gene expression curves within identified subclasses.
  • An optimization algorithm is applied to learn decision score weights using known gene interactions from MGED and literature.

Main Results:

  • PARE predicted 112 (132) pairs of transcriptional regulatory (TC/TD) interactions with high true positive rates (73-77%) compared to qRT-PCR.
  • False positive rates for TC and TD interactions in yeast were low, bounded by 13% and 10% respectively.
  • Several predicted interactions align with known pathways (e.g., involving Sgs1, Srs2, Mus81) and suggest new experimentally testable gene interactions in DNA repair.

Conclusions:

  • The PARE approach provides a robust method for inferring time-lagged genetic interactions from time-course gene expression data.
  • PARE successfully integrates experimental data with prior biological knowledge for enhanced predictive power.
  • The predicted interactions offer valuable insights into gene regulatory networks and potential therapeutic targets, particularly in DNA repair pathways.