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Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships.

Shahab Bakhtiari1, Sadegh Sulaimany2, Mehrdad Talebi3

  • 1Department of Biological Sciences, University of Kurdistan, Sanandaj, Iran.

Cancer Informatics
|July 31, 2020
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Summary
This summary is machine-generated.

This study introduces a computational network approach to predict novel cancer-associated genetic variations (SNPs). The preferential attachment algorithm proved most effective, identifying potential new links between SNPs and cancers for future research.

Keywords:
CancerSNPbipartite networklink prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Genetic variations, like single nucleotide polymorphisms (SNPs), are implicated in cancer susceptibility, but molecular mechanisms remain largely unknown.
  • Existing datasets lack comprehensive bipartite network representations of SNP-Cancer relationships, hindering computational analysis.
  • Link prediction offers a computational graph analysis method to uncover new insights into biological networks.

Purpose of the Study:

  • To construct a bipartite network of cancer and SNP relationships using public databases.
  • To evaluate computational link prediction methods for forecasting novel SNP-Cancer associations.
  • To identify the most robust link prediction algorithm for this purpose.

Main Methods:

  • Data integration from SNPedia and Cancer Research UK databases to build a SNP-Cancer network.
  • Evaluation of various computational link prediction algorithms based on network topology.
  • Application of the preferential attachment (PA) algorithm for predicting new SNP-Cancer relationships.

Main Results:

  • The preferential attachment (PA) algorithm demonstrated the highest robustness in predicting SNP-Cancer relationships.
  • Several predicted SNP-Cancer associations, including rs1801394-Non-small cell lung cancer and rs4880-Non-small cell lung cancer, were identified.
  • Some PA predictions align with recent experimental findings, validating the method's efficacy.

Conclusions:

  • The preferential attachment algorithm is a powerful tool for predicting novel SNP-Cancer associations.
  • Specific predicted associations (e.g., rs1801394-NSCLC, rs4880-NSCLC, rs1805794-Colorectal cancer) warrant further laboratory validation.
  • Computational approaches can significantly advance the discovery of genetic factors in cancer.