MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach.

Liu, Yicheng; Zhang, Tianyun; You, Ningyuan; Wu, Sai; Shen, Ning

Liu, Yicheng; Zhang, Tianyun; You, Ningyuan; Wu, Sai; Shen, Ning.

Afiliación

Liu Y; Department of Hepatobiliary and Pancreatic Surgery, First Affiliated > Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China.
Zhang T; College of Computer Science, Zhejiang University, Yuquan Campus, Zhejiang University, Rd Zheda 38, Xihu District, Hangzhou, 310007, China.
You N; Department of Hepatobiliary and Pancreatic Surgery, First Affiliated > Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China.
Wu S; Department of Hepatobiliary and Pancreatic Surgery, First Affiliated > Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China.
Shen N; Department of Hepatobiliary and Pancreatic Surgery, First Affiliated > Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China. wusai@zju.edu.cn.

Genome Med ; 16(1): 3, 2024 01 08.

Article en En | MEDLINE | ID: mdl-38185709

ABSTRACT

ABSTRACT

Identifying pathogenic variants from the vast majority of nucleotide variation remains a challenge. We present a method named Multimodal Annotation Generated Pathogenic Impact Evaluator (MAGPIE) that predicts the pathogenicity of multi-type variants. MAGPIE uses the ClinVar dataset for training and demonstrates superior performance in both the independent test set and multiple orthogonal validation datasets, accurately predicting variant pathogenicity. Notably, MAGPIE performs best in predicting the pathogenicity of rare variants and highly imbalanced datasets. Overall, results underline the robustness of MAGPIE as a valuable tool for predicting pathogenicity in various types of human genome variations. MAGPIE is available at https//github.com/shenlab-genomics/magpie .

Asunto(s)

Genoma Humano; Aprendizaje Automático; Humanos

Palabras clave

Genomic variation; Machine learning; Multimodal annotation; Pathogenic prediction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genoma Humano / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genome Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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