Artificial intelligence-based approaches for the detection and prioritization of genomic mutations in congenital surgical diseases

Qiongfen Lin; Paul Kwong-Hang Tam; Clara Sze-Man Tang

doi:10.3389/fped.2023.1203289

Artificial intelligence-based approaches for the detection and prioritization of genomic mutations in congenital surgical diseases

Front Pediatr. 2023 Aug 1:11:1203289. doi: 10.3389/fped.2023.1203289. eCollection 2023.

Authors

Qiongfen Lin¹, Paul Kwong-Hang Tam^{1

2}, Clara Sze-Man Tang^{1

3}

Affiliations

¹ Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
² Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.
³ Dr Li Dak-Sum Research Centree, The University of Hong Kong - Karolinska Institutet Collaboration in Regenerative Medicine, Hong Kong, Hong Kong SAR, China.

Abstract

Genetic mutations are critical factors leading to congenital surgical diseases and can be identified through genomic analysis. Early and accurate identification of genetic mutations underlying these conditions is vital for clinical diagnosis and effective treatment. In recent years, artificial intelligence (AI) has been widely applied for analyzing genomic data in various clinical settings, including congenital surgical diseases. This review paper summarizes current state-of-the-art AI-based approaches used in genomic analysis and highlighted some successful applications that deepen our understanding of the etiology of several congenital surgical diseases. We focus on the AI methods designed for the detection of different variant types and the prioritization of deleterious variants located in different genomic regions, aiming to uncover susceptibility genomic mutations contributed to congenital surgical disorders.

Keywords: artificial intelligence; bioinformatics; congenital surgical diseases; variant detection; variant prioritization.

Publication types

Review

Grants and funding

This study was supported by the Theme-based Research Scheme (T12C-714/14-R and T12-712/21-R), the General Research Fund (17113320 and 17113420 to CT), and the Health and Medical Research Fund (PR-HKU-1 to PT, 08193446 and 09201436 to CT).