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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.8K
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,...
17.8K
Asthma-II: Pathophysiology and Classification01:26

Asthma-II: Pathophysiology and Classification

4.0K
Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
4.0K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
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...
15.2K

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Related Experiment Videos

Machine learning approach to single nucleotide polymorphism-based asthma prediction.

Joverlyn Gaudillo1,2, Jae Joseph Russell Rodriguez3, Allen Nazareno1

  • 1Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Philippines.

Plos One
|December 5, 2019
PubMed
Summary

Machine learning models accurately predict asthma risk by identifying genetic markers. This approach enhances disease prediction and diagnosis for complex conditions like asthma.

Related Experiment Videos

Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Complex diseases like asthma involve intricate biological interactions.
  • Predicting individual susceptibility to multifactorial diseases remains a challenge.
  • Machine learning (ML) offers novel approaches for biological data analysis.

Purpose of the Study:

  • To integrate ML models for feature selection and classification to predict asthma susceptibility.
  • To identify single nucleotide polymorphisms (SNPs) associated with asthma risk.
  • To compare the performance of different ML algorithms for asthma prediction.

Main Methods:

  • Implemented Random Forest (RF) and Recursive Feature Elimination (RFE) for SNP feature selection.
  • Utilized K-nearest Neighbor (kNN) and Support Vector Machine (SVM) for sample classification.
  • Integrated RF with SVM (RF-SVM) and RF with kNN (RF-kNN) for predictive modeling.

Main Results:

  • RF demonstrated superior performance over RFE in identifying significant SNPs for asthma.
  • The RF-SVM model achieved the highest performance metrics: 62.5% accuracy, 65.3% precision, and 69% sensitivity.
  • Area Under the Curve (AUC) values of 0.62 for RF-SVM and 0.64 for RF-kNN indicated predictive capability.

Conclusions:

  • ML models, particularly RF-SVM, can effectively predict genetic predisposition to asthma.
  • Integrating ML augments traditional methods for diagnosing complex, multifactorial diseases.
  • This study highlights the potential of ML in personalized medicine and disease risk assessment.