A Novel Approach For Identification Of Single Nucleotide Polymorphisms

Human Genome Project (HGP), Genome Wide Association Studies (GWAS) and The Cancer Genome Atlas (TCGA) are some of the remarkable research endeavours that generated massive amounts of information about Single nucleotide polymorphisms (SNPs) and other genetic variations providing valuable insights for understanding the association of Single nucleotide polymorphisms with diseases. It enables early diagnosis, prevention and treatment planning for diseases. In this study, a novel approach is proposed for identification of SNPs. This approach consists of two techniques: technique I introduces a modified matching strategy for chosen matching algorithms and technique II combines Divide & Conquer technique with technique 1. Performance evaluation of the proposed techniques is performed using performance metrics such as Precision, Recall, F-measure, Execution Time and Resource Utilization (including CPU Utilization and RAM Usage). The proposed techniques overcome most of the research gaps and shortcomings of the existing techniques.

Authors

Publication Details

Journal
Journal of Bioinformatics and Computational Biology
Published
2026-01-30
DOI
https://doi.org/10.1142/s0219720026500034
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
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article

A Novel Approach For Identification Of Single Nucleotide Polymorphisms

S. S. Sohi, Neelofar Sohi
Journal of Bioinformatics and Computational Biology
Machine Learning in Bioinformatics
article

A Novel Approach For Identification Of Single Nucleotide Polymorphisms

S. S. Sohi, Neelofar Sohi
article en

Abstract

Human Genome Project (HGP), Genome Wide Association Studies (GWAS) and The Cancer Genome Atlas (TCGA) are some of the remarkable research endeavours that generated massive amounts of information about Single nucleotide polymorphisms (SNPs) and other genetic variations providing valuable insights for understanding the association of Single nucleotide polymorphisms with diseases. It enables early diagnosis, prevention and treatment planning for diseases. In this study, a novel approach is proposed for identification of SNPs. This approach consists of two techniques: technique I introduces a modified matching strategy for chosen matching algorithms and technique II combines Divide & Conquer technique with technique 1. Performance evaluation of the proposed techniques is performed using performance metrics such as Precision, Recall, F-measure, Execution Time and Resource Utilization (including CPU Utilization and RAM Usage). The proposed techniques overcome most of the research gaps and shortcomings of the existing techniques.

Journal of Bioinformatics and Computational Biology
Openalex Percentile: Top 73%
Machine Learning in Bioinformatics
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