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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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Genetic Variation01:25

Genetic Variation

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.
Genes exist in different versions called alleles, which...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Sequential Support Vector Regression with Embedded Entropy for SNP Selection and Disease Classification.

Yulan Liang1, Arpad Kelemen

  • 1Department of Family and Community Health, University of Maryland, Baltimore 655 W. Lombard Street, Baltimore, MD 21201-1579.

Statistical Analysis and Data Mining
|June 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method for selecting key genetic markers (SNPs) to accurately predict complex diseases. The approach improves diagnostic efficiency and accuracy for genomic research.

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Area of Science:

  • Human genomics and bioinformatics
  • Computational biology and disease prediction
  • Statistical genetics and association studies

Background:

  • Genome-wide association studies (GWAS) identify genetic variations linked to common diseases.
  • Selecting minimal single nucleotide polymorphisms (SNPs) is crucial for cost-effective and rapid diagnostics.
  • Advanced computational methods are needed to enhance SNP selection accuracy for complex diseases.

Purpose of the Study:

  • To develop an advanced computational approach for selecting the most predictive SNPs for common complex diseases.
  • To improve the accuracy and efficiency of disease prediction using genetic data.
  • To address SNP redundancy in genome-wide association studies.

Main Methods:

  • A sequential support vector regression model was developed.
  • An embedded entropy algorithm was utilized for SNP selection.
  • The method was applied to both simulated and real disease datasets for SNP selection and disease classification.

Main Results:

  • The proposed method demonstrated superior performance in disease classification compared to existing methods.
  • It effectively reduced SNP redundancy while maintaining high prediction accuracy.
  • Outperformed Support Vector Machine Recursive Feature Elimination, logistic regression, CART, and logic regression.

Conclusions:

  • The sequential support vector regression with embedded entropy is an effective tool for SNP selection in disease prediction.
  • This method offers a more accurate and efficient approach for genomic diagnostics.
  • It advances the field of computational approaches for human genome research and disease association studies.