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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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,...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.

Behrouz Bokharaeian1, Mohammad Dehghani2, Alberto Diaz3

  • 1Amol University of Special Modern Technologies, Mazandaran, Iran. bokharaeian@gmail.com.

BMC Bioinformatics
|April 12, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed machine learning models to extract associations between single nucleotide polymorphisms (SNPs) and phenotypes from biomedical texts. The PubMedBERT-CNN-LSTM model demonstrated superior performance in identifying these SNP-phenotype relationships and their certainty levels.

Keywords:
Biomedical relation extractionDegree of certainty classificationPhenotypeSNP

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Area of Science:

  • BioNLP
  • Biomedical Informatics
  • Computational Biology

Background:

  • Extracting associations between single nucleotide polymorphisms (SNPs) and phenotypes from biomedical literature is crucial for understanding genetic variations and their effects.
  • Existing methods for extracting mutation-disease affiliations lack the ability to quantify the certainty of SNP-phenotype associations.
  • There is a need for robust methods to identify and rank SNP-phenotype associations, considering their reliability.

Purpose of the Study:

  • To develop and compare machine learning methods for extracting ranked SNP-phenotype associations from biomedical abstracts.
  • To evaluate the performance of various shallow machine learning, kernel-based, rule-based, deep learning, and BERT-based methods.
  • To develop a method for estimating the degree of certainty for extracted SNP-phenotype associations.

Main Methods:

  • Implemented and compared shallow machine learning models (Random Forest, Logistic Regression, Decision Tree).
  • Utilized kernel-based methods (subtree, local context), a rule-based approach, and deep learning models (CNN-LSTM).
  • Developed and evaluated two BERT-based models, including PubMedBERT-LSTM and PubMedBERT-CNN-LSTM, for association extraction and certainty estimation.

Main Results:

  • Deep learning and BERT-based methods outperformed traditional machine learning and kernel-based approaches.
  • The PubMedBERT-LSTM model showed superior performance among the evaluated methods for association extraction.
  • The proposed PubMedBERT-CNN-LSTM method achieved the best performance in both SNP-phenotype association extraction and certainty estimation.

Conclusions:

  • Advanced deep learning and transformer-based models, particularly PubMedBERT variants, are highly effective for extracting SNP-phenotype associations from biomedical text.
  • The developed PubMedBERT-CNN-LSTM model successfully extracts ranked SNP-phenotype associations and estimates their certainty, providing a valuable tool for genetic research.
  • This work addresses the limitation of existing methods by incorporating a measure of certainty for extracted associations, enhancing their utility in biological interpretation.