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Machine learning strategies for discovering genetic patterns in complex and rare conditions

Year: 2024

Type: article

Abstract: Clinically important genetic biomarkers associated with rare disorders are challenging to uncover due to often small sample sizes, high-dimensional variant data, and the presence of complex genetic interactions. Data-driven approaches such as machine learning (ML) methods with feature selection strategies are well-suited to overcome the challenges and can offer key biological insights. We developed GenoMLizer, a genome-wide machine learning (ML) tool for the prioritization of variants as genetic modifiers for the development of symptoms/comorbidities associated with rare disorders. To date, GenoMLizer results have provided novel biological insight and additional support to known genetic associations with symptoms/comorbidities of two diseases, COVID-19 and Turner syndrome.

Source: Iowa Research Online (The University of Iowa)

DOI: 10.25820/etd.007571

Author: Pietan, Lucas

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6964845871

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