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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Genome-wide Association Studies-GWAS01:11

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.
GWAS does not require the identification of the target gene involved in...

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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
05:51

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Published on: June 15, 2011

Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia.

Vanessa Aguiar-Pulido1, José A Seoane, Juan R Rabuñal

  • 1Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, S/N, 15071 A Coruña, Spain. vanesa.aguiar@udc.es

Molecules (Basel, Switzerland)
|July 27, 2010
PubMed
Summary
This summary is machine-generated.

This study uses single nucleotide polymorphisms (SNPs) in specific genes to computationally classify schizophrenia. Machine learning models achieved high accuracy in identifying schizophrenia DNA sequences.

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Area of Science:

  • Genetics
  • Computational Biology
  • Psychiatry

Background:

  • Schizophrenia is a complex disease with significant social impact, necessitating novel diagnostic patterns.
  • Genetic and proteomic markers are crucial for developing accurate diagnostic tools for schizophrenia.
  • Single nucleotide polymorphisms (SNPs) offer potential as inputs for computational disease studies.

Purpose of the Study:

  • To develop and evaluate machine learning classification models for schizophrenia using SNPs from HTR2A and DRD3 genes.
  • To establish Quantitative Genotype-Disease Relationships for schizophrenia diagnosis.
  • To assess the accuracy of classifying schizophrenia DNA sequences.

Main Methods:

  • Utilized single nucleotide polymorphisms (SNPs) from HTR2A and DRD3 genes in Galician schizophrenic patients.
  • Developed computational machine learning classification models.
  • Employed a linear artificial neural network and datasets with simulated negative subjects.

Main Results:

  • The classification models demonstrated the ability to recognize schizophrenia DNA sequences.
  • Accurate classification rates between 78.3% and 93.8% were achieved for schizophrenia subjects.
  • This work presents the first reported Quantitative Genotype-Disease Relationships for schizophrenia using SNPs.

Conclusions:

  • Machine learning models utilizing SNPs from HTR2A and DRD3 genes can effectively classify schizophrenia.
  • The findings suggest a significant relationship between specific genetic variations (SNPs) and schizophrenia.
  • This approach offers a promising avenue for automated DNA sequence-based schizophrenia diagnosis.