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Saez-Matia A, Ibarluzea MG, M-Alicante S, Muguruza-Montero A, Nuñez E, Ramis R, Ballesteros OR, Lasa-Goicuria D, Fons C, Gallego M, Casis O, Leonardo A, Bergara A, Villarroel A. MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants. Int J Mol Sci 2024; 25:2910. [PMID: 38474157 DOI: 10.3390/ijms25052910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.
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Affiliation(s)
| | - Markel G Ibarluzea
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Sara M-Alicante
- Instituto Biofisika, CSIC-UPV/EHU, 48940 Leioa, Spain
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
| | | | - Eider Nuñez
- Instituto Biofisika, CSIC-UPV/EHU, 48940 Leioa, Spain
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
| | - Rafael Ramis
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Oscar R Ballesteros
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Centro de Física de Materiales CFM, CSIC-UPV/EHU, 20018 Donostia, Spain
| | | | - Carmen Fons
- Pediatric Neurology Department, Sant Joan de Déu Hospital, Institut de Recerca Sant Joan de Déu, Barcelona University, 08950 Barcelona, Spain
| | - Mónica Gallego
- Departamento de Fisiología, Universidad del País Vasco, UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Oscar Casis
- Departamento de Fisiología, Universidad del País Vasco, UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Aritz Leonardo
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Aitor Bergara
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
- Centro de Física de Materiales CFM, CSIC-UPV/EHU, 20018 Donostia, Spain
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