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

Author:

Ahmad Rahaf M12ORCID,Ali Bassam R12ORCID,Al-Jasmi Fatma1234ORCID,Sinnott Richard O56ORCID,Al Dhaheri Noura1234ORCID,Mohamad Mohd Saberi12ORCID

Affiliation:

1. Health Data Science Lab , Department of Genetics and Genomics, College of Medical and Health Sciences, , Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates

2. United Arab Emirates University , Department of Genetics and Genomics, College of Medical and Health Sciences, , Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates

3. Division of Metabolic Genetics , Department of Pediatrics, , Al Ain, United Arab Emirates

4. Tawam Hospital , Department of Pediatrics, , Al Ain, United Arab Emirates

5. School of Computing and Information System , Faculty of Engineering and Information Technology, , Melbourne, Victoria, Australia

6. The University of Melbourne , Faculty of Engineering and Information Technology, , Melbourne, Victoria, Australia

Abstract

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.

Funder

United Arab Emirates University

Research Start-up Program

ASPIRE

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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