Citation

Ahmad, Rahaf M. and Ali, Bassam R. and Al-Jasmi, Fatma and Al Dhaheri, Noura and Al Turki, Saeed and Kizhakkedath, Praseetha and Mohamad, Mohd Saberi (2024) AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes. Human Genomics, 18 (1). ISSN 1479-7364

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Abstract

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived insilico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of diseasespecific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy=0.73), Meta-RNN (Accuracy=0.72), ClinPred (Accuracy=0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy=0.70). While on HGMD dataset they were ClinPred (Accuracy=0.72), MetaRNN (Accuracy=0.71), CADD (Accuracy=0.69), Fathmm-MKL (Accuracy=0.68), and Fathmm-XF (Accuracy=0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, Breast cancer
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:46
Last Modified:	01 Oct 2024 01:46
URII:	http://shdl.mmu.edu.my/id/eprint/13008

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