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Briefings in bioinformatics
11 22, 2023;25 (1):

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.

Rahaf M Ahmad, Bassam R Ali, Fatma Al-Jasmi, Richard O Sinnott, Noura Al Dhaheri, Mohd Saberi Mohamad
Author Information
  1. Rahaf M Ahmad: Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates. ORCID
  2. Bassam R Ali: Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates. ORCID
  3. Fatma Al-Jasmi: Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates. ORCID
  4. Richard O Sinnott: School of Computing and Information System, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia. ORCID
  5. Noura Al Dhaheri: Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates. ORCID
  6. Mohd Saberi Mohamad: Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates. ORCID
PMID: 38149678 DOI: 10.1093/bib/bbad479

Abstract

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

Keywords

artificial intelligence breast cancer data science genetic variants database machine learning pathogenicity prediction

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Grants

  1. 12R111/United Arab Emirates University
  2. 12M109/Research Start-up Program
  3. /ASPIRE

MeSH Term

Humans
Female
Breast Neoplasms
Virulence
Databases, Factual
Risk Factors
Machine Learning
Review Journal Article Research Support, Non-U.S. Gov't

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