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Computational Approaches to assess the Impact of Non-Synonymous Single Nucleotide Variations on Diseases

Author(s): Alan R. Henry*

Nonsynonymous Single-Nucleotide Variants (nsSNVs) and mutations can disrupt protein stability by affecting genotype and phenotype. Changes in protein stability can lead to illnesses like cancer. The discovery of nsSNVs and mutations can be a useful tool for early diagnosis of the disease. Many researches have described various solitary and consensus prediction methods based on various Machine Learning Techniques (MLTs) and distinct datasets. Many researchers are developing novel techniques to anticipate pathogenic variants, as well as Meta-tools that combine many of them to improve their predictive potential. In addition, the protein stability predictors were screened for various types of computational techniques in the state-of-the-art, as well as methods for predicting the influence of both coding and noncoding variations. Targeted at bioinformaticians interested in identifying regulatory variants, geneticists, molecular biologists interested in learning more about the nature and functional role of such variants from a functional standpoint, and clinicians interested in learning about variants in humans linked to a specific disease and figuring out what to do next to figure out how they affect the underlying mechanisms.


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